Career preparation plays an important role in successful educational transitions such as the transition to post-comprehensive education (see Anderson et al., 2000). Career preparation is a combination of career decidedness, planning and confidence (Skorikov, 2006) and is a major developmental task for adolescents (Savickas, 1999; Super, 1990). Enhancing career preparation can support adolescents’ transitions to post-comprehensive education. We know from earlier research that intensive group interventions based on peer learning can effectively enhance career preparation, especially among students at risk of depression or with learning difficulties (Vuori et al., 2008).

There is some indication in earlier studies (Vuori et al., 2008; Koivisto et al., 2011; Jokisaari & Vuori, 2011) that school-based intensive group interventions may support career transitions. However, intensive interventions may require substantial resources in terms of competing curricula demands and adjustments to school schedules. These factors may act as barriers to implementation and hinder the sustainability of intervention programmes in schools. Weatherson et al. (2017) found that the lack of time in school schedules due to competing curricular demands was a key barrier to implementing interventions in schools. Gee et al. (2021) explored the factors that influence the successful implementation of psychological interventions in schools. According to their study, the extent to which the intervention could be flexibly deployed to cause minimal disruption to other school routines was important for its implementation and sustainability. Forman et al. (2009) also highlighted that integrating interventions into the curriculum is important for facilitating the implementation and sustainability of evidence-based practices (EBPs) in schools. Earlier studies have suggested that tailoring implementation strategies may offer opportunities to improve the sustainability of school-based EBPs (see Walsh-Bailey et al., 2021). However, some earlier studies have also pointed out that adaptions and modifications may reduce the efficacy of EBPs (McCrabb et al., 2019). So far, few studies have explored whether modifications aiming to facilitate scaling-up have an impact on the desired benefits of school-based interventions.

Our study conceptually replicated an earlier study investigating the efficacy of an intensively implemented, school-based programme for enhancing study career choices among middle schoolers (Vuori et al., 2008). In the replication, we examined the same intervention mechanism as the earlier study, but we adjusted the delivery approach to see whether the positive effects would also be replicated using different delivery features (see Coyne et al., 2016; see Mathews et al., 2018). The main difference between the current and the prior study is that in the current study, the intervention programme was integrated into school routines and was implemented as part of regular guidance counselling throughout the school year. The rationale for these modifications was the aim to identify the approaches that facilitated scaling-up the intervention. We investigated whether the modified intervention delivery approach was effective, as it was in the original programme, in enhancing career preparation. Furthermore, we assessed the implementation of the intervention’s active ingredients as indicators of implementation fidelity.

Conceptual replication

Earlier studies (see Hudson, 2021) have made a distinction between conceptual replication and direct (or exact) replication studies. The latter seeks to duplicate an original study in all aspects, whereas the former modifies the circumstances, delivery process, or components of an experimental process (Coyne et al., 2016; Hudson, 2021). According to Coyne et al., (2016, p. 247), a conceptual replication study ‘helps to define the conditions under which an intervention is more or less efficacious’. Conceptual replications use a study approach to determine whether the positive intervention effects can be replicated using different delivery features. In this respect, our study was a conceptual rather than a direct replication. We implemented the previously developed career preparation peer learning intervention (Vuori et al., 2008) in the same school settings and evaluated its efficacy using the same outcome measure. We also adapted the delivery process to facilitate scaling-up the intervention.

Peer learning as a means of career preparation

Peer learning involves acquiring knowledge and skills through active help and support among status equals or matched companions (Topping, 2005). Hanson et al. (2016) highlight the common aspects of peer learning in schools. First, pupils play an active role in the learning process and are not viewed as mere passive recipients of knowledge. Second, teachers take on the role of facilitator. Third, the learning process is based on communication and interaction between the pupils as they share ideas and experiences and support each other. Peer learning involves making meaning through interactions with peers (Hanson et al., 2016). Studies of the effects of peer learning have consistently shown that, across a variety of educational settings and cultural backgrounds, working with peers is associated with beneficial learning outcomes (Hanson et al., 2016; see Tenenbaum et al., 2020 for meta-analysis). Evidence also shows that peer learning is positively associated with adolescents’ psychological or affective well-being: emotional maturity, strong personal identity, ability to cope with adversity, resilience, social competencies, self-confidence, independence and autonomy, and higher self-esteem, for example (Hanson et al., 2016; Johnson & Johnson, 1989, 2009; Tang et al., 2022).

Peers can act as positive role models and peer learning has the potential to foster positive school experiences among students (see Schmalenbach et al., 2022). Ginsburg-Block et al. (2006) highlighted that peer learning has an especially beneficial impact on adolescents, who may be identified as more vulnerable. It seems that peer learning may possibly provide the most benefit to those with lower academic achievement. Birch and Li (2009), Dancer et al. (2015), and Kochenour et al. (1997) all concluded that enrolment in a peer learning-based programme had a positive effect on university students’ final grades, and that this effect was stronger among students who were at the lower end of grade distribution.

In a previous RCT, Koivisto et al. (2011) showed that the peer learning process can also be used effectively in career preparation. They demonstrated that an intervention programme, based on the peer learning process, directly improved pupils’ career choice preparedness, which in turn increased positive attitudes towards career planning among ninth graders completing comprehensive education. In addition, at six-month follow-up, the programme had increased the number of guidance counsellors, called network ties (Jokisaari & Vuori, 2011). Among the pupils who were at risk of depression at baseline, the programme decreased symptoms of depression (Vuori et al., 2008). Among the pupils who were at risk of depression and had learning difficulties at baseline, the programme decreased school burnout (Vuori et al., 2008). In this original intervention programme, the intervention mechanism was based on five core elements: career management skills training, active learning methods, a supportive learning environment, inoculation against setbacks, and skilled trainers. In the earlier RCT trial, the group intervention took place over a total of 15 h on four to five consecutive days. The programme was implemented by two instructors in such a way that counsellors working in basic education trained the groups together with counsellors from vocational institutes.

Other studies have also found school-based interventions based on peer learning and peer support processes to be effective in terms of preparing for a future work career. Nelwati et al. (2020) observed that the professional competence of undergraduate nursing students increased after the peer learning programme intervention. Gai et al. (2022) also found that an intervention based on peer coaching and peer learning had a beneficial impact on first-year college students’ career adaptability, including control over decision-making, curiosity to explore career opportunities, and confidence to deal with career-related barriers. Their study revealed peer learning benefits, especially in the risk group with low career adaptability scores.

Career choice preparedness

The concept of career choice preparedness, which is the focus of this study, captures the combined aspects of adolescents’ career preparation, i.e., decidedness, planning and confidence. Career choice preparedness is defined as ‘the readiness to take advantage of opportunities and the readiness to deal with barriers and setbacks in the domain of career choice’ (Koivisto et al., 2011), and consists of career choice self-efficacy and inoculation against setbacks.

In general, self-efficacy refers to ‘beliefs in one’s capabilities to organize and execute the course of action required to produce given attainments’ (Bandura, 1997, p. 3) and is the basis of social cognitive theory. Domain-specific self-efficacies have an impact on, for example, how much effort one exerts to reach a set goal, one’s resilience to adversity, and how much stress one experiences in coping with environmental demands. Self-efficacies also play an important role in converting intentions into actions (Ajzen, 1991). In our study, self-efficacy beliefs are related to decision-making and information-seeking.

Inoculation against setbacks, in turn, refers to an individual’s anticipatory stress management skills (Meichenbaum, 1985). Adolescents typically encounter obstacles or setbacks during educational transitions (Lent et al., 1994). For a smooth career transition, adolescents must be prepared to face potential setbacks and be taught the skills to cope with these. By preparing for setbacks, adolescents increase their resilience and develop problem-solving skills when confronted with possible obstacles during their educational transition. This helps them maintain their motivation and adopt an active role in overcoming difficulties (see Meichenbaum, 1985).

In summary, self-efficacy beliefs and inoculation against setbacks (i.e., career choice preparedness) are important, individual-level resources that contribute to adolescents’ successful educational transitions. Preparedness serves as an individual resource for adolescents’ educational careers, helping them make choices, stay on target, and remain motivated, even in potentially challenging situations. An earlier intervention study (Koivisto et al., 2011) demonstrated how career choice preparedness increases positive attitudes towards career planning. Supporting preparedness is especially important before and during the transitions between education levels, because adolescents often experience a decline in self-efficacy during school transitions (see Hungnes et al., 2022; Eccles, 2004),

Advancing scalability of school-based interventions

An important area of research is how to improve the scalability of school-based interventions. Milat et al. (2020) define the ‘scalability of an intervention as the ability of an intervention shown to be efficacious to reach a greater proportion of the eligible population while retaining effectiveness’. Earlier research has emphasized that the extent to which a school can incorporate EBP into its existing practices and routines plays an important part in sustaining implementation activities (see Durlak & Dupre, 2008). Also, Klinger et al. (2013) have highlighted that, for the new practice to continue over time, sustainability requires its integration into the normal routines of the school. For this study, we adapted the delivery approach of a career preparedness peer learning method to advance the scalability of the intervention. The modifications included a shift from intensive implementation (intervention sessions implemented on consecutive days) to longer-term implementation intended to improve the intervention’s fit with the school’s routines and curriculum. This involved delivering intervention sessions as part of regular guidance counselling lessons.

Study aim and hypothesis

The aim of the present study was to conduct a conceptual replication study of an earlier RCT trial (Vuori et al., 2008; Koivisto et al., 2011) study and to evaluate whether the peer learning group method improved career choice preparedness for adolescents completing comprehensive education. We aimed to extend previous study results by implementing the same intervention techniques but with changes to the intervention delivery approach. The rationale for the modifications was to advance the scalability of the intervention. A previous study by Koivisto et al. (2011) demonstrated that an intensively delivered programme significantly enhanced career choice preparedness as its planned primary proximal effect. Thus, we also hypothesized the effects of the replication:

Study hypothesis: participation in the intervention programme will significantly increase career choice preparedness.

In conceptual replications, it is important to examine both the intervention effects and implementation fidelity (Coyne et al., 2016), the latter of which refers to the quality of delivery to attain beneficial effects (Dunst et al., 2013). Although previous studies have stressed that intervention modifications do not necessarily deviate from the EPB’s original mechanism or theory (Kemp, 2016), it is nevertheless important to confirm that the modifications do not interfere with implementation fidelity. Thus, we examined whether the student perceptions of the active ingredients of the intervention programme differed in terms of implementation fidelity from those in the earlier study based on the original delivery approach.


Context: the Finnish education system

Finnish adolescents graduate from basic education after nine years of school at the age of 15–16. At the beginning of their last spring semester, ninth graders must decide on their further education. Although at the time of the study further education was voluntary, it was a prerequisite for unemployment benefits and thus, for example, only 1.0% of basic education graduates did not apply for further education in 2018. The main choice is between high school, leading to the matriculation examination (53.7% of applications in 2018) and vocational college, leading to vocational qualifications (43.7% in 2018). The former is often referred to as the academic track and the latter as the vocational track. This is because the matriculation examination lays the foundation for further studies at universities whereas the vocational qualification enables transition to working life or further studies at universities of applied sciences. The likelihood of dropping out from education is higher for adolescents with poor academic achievement (Archambault et al., 2017) and immigrant backgrounds (Kilpi-Jakonen, 2011). The application for further studies is called the Joint Application and its annual deadline is in March. To evaluate the effect of the programme, we used a cluster RCT design, which clustered pupils within classrooms and within schools. The schools with the highest dropout rates of each participating city were selected in pairs for randomization into intervention and control schools. Earlier studies indicate that learning methods based on peer learning benefit lower-achieving students in particular. Targeting the intervention at schools with a higher dropout rate may help these schools strengthen their student guidance. After randomization, the schools were invited to participate in the study. About half of the classes in the control schools were randomized into the control group, which received regular guidance counselling.

Recruitment and sample

The trial was carried out over three academic years (2016–2017, 2017–2018, and 2018–2019), so that different schools participated in each academic year. Figure 1 presents a diagram of the recruitment and retention of schools, classes, and pupils.

Fig. 1
figure 1

A diagram of the recruitment and retention of schools, classes, and pupils

Schools and classes

Altogether 34 school pairs from eight cities in Southern Finland were invited to participate in the study. Of these, 26 intervention schools and 23 control schools enrolled. However, five intervention schools and two control schools were excluded, as their matched pair school did not participate, and we wanted to preserve the integrity of the study design (Donner & Klar, 2004). Thus, the sample at the beginning of the trial consisted of 21 intervention schools and 21 control schools. During the trial, one intervention school and one control school withdrew from the study, meaning that we had to also exclude their matched pair school. The final sample in this study consisted of 38 schools (19 intervention and 19 control schools), of which 37 classes implemented the programme, and 57 classes acted as controls.


All the pupils in the participating classes were invited to fill out a questionnaire (either online or on paper) during the school day twice: first at the beginning of the autumn semester (September–August, T1) and then after the intervention programme (January–February, T2). Completing the questionnaires was voluntary and based on informed consent. Only participants who provided data for career choice preparedness at T1 were included in the outcome analyses. Due to missing values, 203 pupils were excluded from the final analyses. These were more often boys than girls (p = 0.048), non-native immigrants than native Finns (p < 0.001) and had a lower eighth grade point average (GPA) (M = 7.85) than those in the final sample (M = 8.22, p < 0.001). A larger proportion of excluded participants belonged to the control group (64.9% vs 35.1%). The final sample of the pupils analysed in this study consisted of 470 pupils in the intervention programme and 598 pupils in the control group (total n = 1068). Of these, 55.3% were girls, 14.2% had a non-native immigrant background, and 13.2% were diagnosed with dyslexia or learning difficulties. Compared to the general Finnish ninth grader population, girls and pupils with non-native immigrant backgrounds were overrepresented in the sample (population percentages 49.0% and 6.4% respectively). The share of pupils with diagnosed dyslexia or learning difficulties corresponded well to the national share of 5–20%, depending on the source of information (Mikkonen et al., 2015).

Effectiveness of randomization

Table 1 presents the baseline characteristics of the participants in the programme and control groups. Despite randomization, those in the intervention group had higher eighth grade GPA and lower levels of baseline study burnout than those in the control group.

Table 1 Baseline characteristics of pupils in intervention and control group

In line with the intention to treat principle (McCoy, 2017), the analyses included all the available cases, regardless of whether the pupils had participated full time in the programme. Based on self-reports, 87.9% of the programme group estimated that they had participated in more than half of the peer learning sessions.

Peer learning sessions

The peer learning sessions were developed to enhance career preparation and designed on the basis of the early developed intervention (Vuori et al., 2008). The schools’ guidance counsellors held eight peer learning sessions (lasting 45 min each) as part of regular schoolwork during the autumn semester and the beginning of the spring semester before the Joint Application. The schools’ guidance counsellors followed the instruction manual when implementing the intervention. The number of sessions was set at eight, so that all the important topics would be covered, but that the sessions could still be held as part of the guidance counselling study programme. Five active ingredients formed the basis of this programme: trainer skills, utilization of active learning from peers, a supportive learning environment, career preparation skills training, and inoculation against setbacks.

Trainer skills

Before implementing the programme, the guidance counsellors received two days of training. The training introduced the content and the core elements of the programme. The guidance counsellors also did some of the programme’s exercises, taking the role of pupil. Each counsellor received a facilitator’s guidebook, which included detailed descriptions of the peer learning sessions and principles. A total of 31 guidance counsellors were trained during the project.

Active learning

The guidance counsellors were facilitators during the sessions and let the pupils work in pairs and small groups, creating solutions themselves. The guidance counsellor’s role was to introduce the exercises, to observe, to activate, and to guide the pupils towards the desired conclusions.

Supportive learning environment

In line with Bandura’s social cognitive theory (1986), an important task of the guidance counsellors in this programme was to create a supportive learning environment by maintaining the pupils’ trust in the trainers, each other, and the programme; and by giving directly targeted, well-timed and well-argued feedback to the pupils. In this supportive, positive learning environment, the pupils were motivated to provide mutual support and learn from each other.

Career preparation skills training

During the peer learning sessions, the pupils identified their personal strengths and skills, and discussed how skills and abilities acquired during leisure time could be used in studies or in working life. The pupils got to know the Finnish education system, considered potentially interesting occupations, and how different occupations may require the same interests and skills. They also pondered difficult group situations (working in a group and getting to know new classmates) and solutions to these. They practised verbalizing their skills and strengths in an interview, and at the end of the programme, they practised setting goals. Everyone set a goal for their further study career.

Inoculation against setbacks

As all pupils inevitably encounter some setbacks or barriers during their educational careers (e.g., lack of social support), they were guided through problem-solving processes based on Meichenbaum’s model (1985). Inoculation against setbacks was accomplished through identifying possible setbacks and barriers, generating solutions to them, and practising overcoming them.

This study made several changes to the delivery features of the original intervention programme to bring the intervention more in line with school routines. The peer learning sessions were integrated more effectively into the local school curriculum and regular lessons, by (a) excluding information interviews with guest older students from upper secondary level or vocational institutes and a visit to a local labour office, (b) using greater spacing between sessions and lengthening the delivery timeframe (from consecutive days during one school week to five to six months during one school semester, (c) using only each schools’ own guidance counsellors as facilitators, and (d) shortening the training of the intervention providers (from 3 to 2 days). In other respects, the intervention programme and sessions included instructional techniques and guidelines equivalent to those of the earlier group intervention.


Career choice preparedness was measured on a 12-item scale developed by Koivisto et al. (2011). The scale assesses both self-efficacies and inoculation against setbacks. The following introduction preceded the ten self-efficacy items: ‘Next, we would like to know how well you believe you can perform in different education- and career choice-related tasks. Do you believe you are able to…. The seven items for assessing self-efficacy beliefs in decision-making were the following: (1) ‘Identify which of your personal strengths help you make education- and vocational career-related choices’, (2) ‘Identify which of your personal interests are related to a particular profession and work career’, (3) ‘Choose the most personally suitable sector of education for yourself’, (4) ‘Identify the school subjects in which success will promote the realization of your future career plans’, (5) ‘Tell us your dream occupations’, (6) ‘Describe the main characteristics of your dream occupation or career’, (7) ‘Assess the effect of the choices you make now on your future education and work career’. The three items for assessing information-seeking self-efficacy were the following: Do you feel you are able to… (1) ‘ask your friends, acquaintances or relatives about the occupational and educational opportunities that interest you?’, (2) ‘contact your guidance counsellor or teachers to obtain guidance and information on issues related to your prospective educational and work career?’, (3) ‘search for information on issues related to your prospective education and work career?’. The pupils answered using a seven-point scale from 1 (very poorly) to 7 (very well). The following two items assessed inoculation against setbacks: (1) ‘Do you have ideas or alternative plans in case you do not get accepted at your first-choice education institution?’ and (2) ‘Do you have an ideas or alternative plans for any possible setbacks you may face in your studies?’. The pupils answered using a seven-point scale from 1 (very few) to 7 (very many). The Cronbach alpha coefficient for internal reliability of the Career Choice Preparedness scale was 0.89 at T1 and 0.88 T2.

Our analysis also included implementation fidelity measure. At T2, the pupils rated various aspects of the implementation of the My Path programme using a seven-point scale (1 = not at all, 7 = very often; Table 2). To enable us to compare the students’ perceptions of implementation fidelity, we opted to assess implementation using the same evaluation method as that used in the previous intervention study (Vuori et al., 2008). Previous studies have suggested that pupils’ ratings may offer a more reliable assessment result than intervention-delivered self-reports (see Berkel et al., 2011; Lillehoj et al., 2004). Observing the sessions was not feasible resource wise.

We used gender, immigrant background, eighth grade GPA, and study burnout at T1 as control variables in the analyses, based on the results of the effectiveness of randomization. Gender (0 = girl, 1 = boy) and immigrant background (0 = native, 1 = non-native) were dichotomous variables. More specifically, immigrant background was classed as native for pupils who themselves, as well as their parents, were born in Finland; and non-native for pupils who either themselves or at least one of their parents were born somewhere other than Finland. Eighth grade GPA (4–10) was a continuous variable and reported by the pupils themselves. Study burnout was measured using six items from the School-Burnout Inventory (e.g., ‘I feel overwhelmed by my studies’; Salmela-Aro et al., 2009). Answers were given on a six-point scale from 1 (completely disagree) to 6 (completely agree). The Cronbach alpha coefficient for the internal reliability of the School-Burnout Inventory was 0.83 at T1 and 0.84 at T2.

Data analysis

We conducted a power analysis using a two-level hierarchical design with covariates (see, Hedges & Rhoads, 2010) and used an effect size of 0.29 as a reference from an earlier RCT (Koivisto et al., 2011). With a significance criterion of α = 0.05 and power of = 0.80, the recommended sample size to identify an intervention effect at the level of previous research was approximately 400 students from 14 schools. First, to estimate implementation fidelity, we analysed the student perceptions of the implementation of the core components of the intervention programme. Second, we assessed the unidimensionality of the 12-item career choice preparedness measure. We tested the CFA models using MPlus 8.0 with maximum likelihood parameter estimates and robust standard errors (MLR). Model fit was assessed using the comparative fit index (CFI > 0.95), the root mean square error of approximation (RMSEA < 0.07), and the standardized root mean square residual (SRMR < 0.08). The selected cutoff values were based on Hooper et al. (2008). Third, to test our hypotheses, we analysed the effect of the programme on career choice preparedness using linear mixed models (LMM) with SPSS 26.0. The data had two time points (baseline T1 and short-term follow-up T2). Only the participants who completed the baseline questionnaire and provided data for career choice preparedness at T1 and the control variables were included in the analyses. LMM was able to estimate the model parameters with missing values in the dependent variable at T2. Furthermore, the data had a clustered structure in which pupils were nested within classes. The classes were also nested within schools, but as the intraclass correlation (ICC; representing the proportion of variance in the outcome that is between schools) was 0.01 and the design effect (1 + (average cluster size − 1) * ICC) was < 2, suggesting minimal bias towards standard errors due to clustering pupils within schools, we decided not to take into account the school level (Muthén & Satorra, 1995). The main effect model included control variables (gender, immigrant background, eighth grade GPA, study burnout), the time variable (T1 and T2), the condition variable (dummy coded as 0 = control vs 1 = intervention programme) and time × condition interactions as fixed effects, and class as a random effect. The models were random intercept models with an identity covariance structure for the random effect and an unstructured covariance structure for the repeated effect. Cohen’s d effect sizes, as described by Morris (2008), were calculated to estimate between-group differences in group mean changes over time.


Implementation fidelity

On a scale of 1 to 7, student perceptions of the intervention’s integrity varied between 3.20 and 4.85. Table 2 demonstrates that the student perceptions of implementation fidelity were higher in the earlier intervention study, based on intensive delivery format (Vuori et al., 2008). Implementation quality showed poorer results in all aspects of the delivery process, including perceived learning atmosphere, the amount of training in career skills, the amount of inoculation training, and the perceived trainer skills. In addition, a smaller proportion of the students who participated in the current study felt that the content of the intervention suited their own needs (‘did the discussions generally suit your needs and situation?’ M = 3.96 [SD 1.62] vs M = 4.90 [SD 1.74]).

Table 2 Implementation fidelity evaluations

Main effect analyses

Table 3 presents the Pearson’s correlation coefficients of the study variables. Table 4, in turn, shows the baseline (T1) and follow-up (T2) unadjusted means of career choice preparedness for the intervention group and the control group.

Table 3 Pearson’s correlation coefficients of study variables (n = 1068)
Table 4 Unadjusted means of career choice preparedness at T1 and T2 for intervention, and control condition

Estimating a one-factor model of career choice preparedness provided results that fit the model well at T1, with χ2(51, n = 1068) = 303.015, p < 0.001, CFI = 0.957; RMSEA = 0.055; SRMR = 0.034 and at T2 with χ2(51, n = 814) = 283.127, p < 0.001, CFI = 0.955; RMSEA = 0.058; SRMR = 0.038. Sufficient levels of unidimensionality allowed us to use self-efficacy and inoculation measures as indicators of the global construct of preparedness (see Bandalos, 2002). Table 5 presents the parameter estimates, p-values, and confidence intervals of the LMM analyses. The assumption of homoscedasticity was met, and the residuals were normally distributed in all the models. As we expected, the effect of the intervention programme on career choice preparedness was positive and statistically significant (estimate 0.14, p = 0.021). Cohen’s d for this effect was 0.12.

Table 5 Results of random intercept models (class as random effect) for effects of the intervention programme (n = 1068)

We calculated the standardized effect size (Morris, 2008) using the results reported in the earlier RCT, based on the intensive implementation format (Koivisto et al., 2011). In the original study, the effect size of career choice preparedness was 0.29, suggesting that the impact of the intensive implementation used in the original studies was stronger.


Advancing scalability is an important goal in development school-based EBPs. In this conceptual replication study, our modifications aimed to improve the intervention–school context fit by lengthening the timeframe for completing the career intervention programme and implementing intervention sessions as part of regular guidance counselling lessons.

This study provided promising results. The modified version of the peer learning intervention programme had a beneficial effect on the adolescents’ career choice preparedness. The result suggests that the modified intervention programme preserved its identifiable positive effects, although it was now more integrated into regular school lessons and had undergone significant changes in its delivery features. Therefore, our study results support earlier study findings regarding the efficacy of peer learning methods in career preparation. In terms of observed preparedness levels, we found that, in the absence of intervention delivery, the preparedness of the control group pupils decreased from baseline to follow-up. This indicates that the intervention process protected the pupils’ level of career choice preparedness against decreasing. It seems that as concrete decision-making regarding career choice and educational transition phase (here Joint Application) gets closer, career choice preparedness may decrease. This is in line with the results of previous studies that have suggested that young people’s self-efficacy may reduce educational transitions (see Hungnes et al., 2022; Eccles, 2004). Our study showed that career choice preparedness can be supported through structured peer learning and supportive interventions implemented in connection with educational transition phases.

Furthermore, our research shows that the intervention mechanism is also present when intervention sessions are implemented over a longer time period of a whole school year. This result provides important information for advancing the scalability of school-based career counselling interventions. Our study shows that by fostering the methods of peer learning and peer support, it is possible to support career choice preparedness during educational transitions without intensive intervention implementation.

However, our results also indicate that dismissing intensive implementation leads to a somewhat reduced effect size. It should be noted that already in the original study, the effect of the intervention was classified as small. It is important to acknowledge that a scalable intervention may have greater potential to generate positive overall effects due to having better premises to reach a larger population. An intervention that produces a small effect can be significant if it is scalable, easy to implement and cost-effective (see Harris, 2009; Kraft, 2020). It is also important to consider the potential benefits of the psychological intervention effect in the longer term. Even small or marginal intervention effects on psychological resources such as preparedness may produce other benefits that accumulate and develop over time as pupils progress along their educational path. School-based intervention effects may translate into greater educational and wellbeing effects later in life (see Taylor et al, 2017; see Clarke et al., 2021).

In comparison to the original study, the pupils’ intervention integrity ratings were lower for integrity evaluations. Prior research (Durlak & Dupre, 2008) has shown that the quality of intervention delivery is associated with the intervention outcomes. Hence, the implementation results may to some extent explain the reduced effect size. Conducting the intervention on consecutive days over one week may result in better conditions for active learning and a more supportive atmosphere for career choice preparation. When the intervention programme is delivered over a longer period of time, it may be more difficult for the participants to connect insights from previous meetings to new group discussions and group activities. A supportive learning atmosphere and active learning process are important aspects in peer learning.

Furthermore, to save the time resources of school staff, guidance counsellor training was condensed from three to two days. Shortening the training of intervention providers may have contributed to the decline in implementation fidelity. In the future, counsellor training should pay special attention to skills in stimulating the active learning process. This may require extending the training to three days. Previous research (Durlak & Dupre, 2008) has highlighted that the skill proficiency of intervention providers is a key factor for achieving implementation fidelity in school-based interventions. When considering ways in which to reduce the resources required to implement EBPs (e.g., time resources), it is essential that adequate implementation training is not compromised.

Limitations and strengths

Our conceptual replicate study applied the golden standard for efficacy studies (Cartwright, 2011), namely RCT. Conceptual replicate studies using the RCT design are rare in the field of school-based intervention studies and provide evidence of whether positive effects from an earlier intervention study can be replicated under different conditions. Nevertheless, some limitations deserve attention. It should be noted that we compared the results of cluster-level randomized trials and individual-level randomized trials. Leyrat et al., (2019) pointed out that cluster-level randomized and individual-level randomized trials are often pooled in meta-analyses. Using a meta-epidemiological study, Leyrat et al. (2019) compared the intervention effect estimates of cluster-level randomized and individual-level randomized trials. They found that the effect estimates were marginally more favourable for individual-level randomized trials with continuous outcomes. As a result, the comparison of experiments performed with different study designs is not completely straightforward and study design may affect the results. However, the current RCT and earlier intervention study had sufficient similarities to enable a comparison between the results and we were able to draw conclusions regarding the modifications of the intervention. One limitation in the current study is the lack of longer-term monitoring of the intervention’s effectiveness. In the current study, we evaluated the proximal intervention effect, which plays a key role in further effectiveness. Missing data is another limitation. The results may also be affected by the fact that the excluded participants differed from those included in terms of background variables such as gender and academic achievement. This may have reduced the observed effect size, as previous studies have shown that peer learning has a beneficial impact, especially on those with lower academic achievement.


This study adds to the evidence base that peer learning career interventions are an important tool for enhancing career choice preparedness among adolescents during educational transitions. The importance of the peer learning career intervention is emphasized by the fact that it seems to protect career choice preparedness, including self-efficacy, against decreasing in the critical transition phase of moving on to secondary education. Furthermore, our study showed that the intervention’s beneficial proximal outcome was maintained despite the intervention sessions being integrated into regular career guidance lessons without an intensive delivery approach. Our study results have implications for school-based career counselling interventions, as schools seek to identify evidence-based methods that are cost-efficient and sustainable, and can be integrated into school routines. However, it should be noted that promoting scalability by modifying the intensity of the delivery approach may lead to a reduction in the effects of a career preparation intervention based on peer learning.