1 Introduction

Palliative care focuses on quality of life. Patients’ and their loved ones’ well-being is central to the care provided, which mobilizes a large professional multidisciplinary team as well as volunteers (Clark, 2007). Practicing palliative care can be challenging for both staff (Sardiwalla et al., 2007) and volunteers (Claxton-Oldfield, 2016). Indeed, the literature points to difficulties at the emotional and organizational levels, which can be stressful for volunteers (Varay et al., 2022). The most common relate to facing pain, physical or psychological decline, and death (Morris et al., 2013), to the role of volunteers, perceived as poorly defined (Delaloye et al., 2015), ambiguous (Paradis et al., 1987), and sometimes as peripheral (Burbeck et al., 2014), the communication with patients, families, or staff (Pesut et al., 2014), the integration within the medical team (Laperle & Ummel, 2019; Vanderstichelen et al., 2019), or the information, training and support offered (Morris et al., 2013). Volunteers express needs that echo these difficulties: needs for information, training, delineation of their place in the care system, communication, integration into the professional multidisciplinary team, recognition and support (Varay et al., 2022). However, volunteers involved in palliative care report being able to handle the stress associated with their commitment (Dein & Abbas, 2005). They mobilize internal resources, such as their character strengths or their natural gifts (Guirguis-Younger & Grafanaki, 2008), and external resources, as they insist on the importance of relationships with patients, staff and other volunteers (Coleman et al., 2022). They use both problem- and emotion-focused coping strategies (Brown, 2011) and feel they receive the support they need (Dein & Abbas, 2005). Thus, most palliative care volunteers consider their task to be fulfilling (Claxton-Oldfield & Claxton-Oldfield, 2007). They mention motivations such as pleasure in helping others, developing social relationships, learning, and maturing (Stelzer & Lang, 2016). A literature review by Claxton-Oldfield (2015), revealed that volunteers find palliative care intervention a chance to be useful and an opportunity for personal growth, and that they feel receiving more than giving. The emotional experience of palliative care volunteers was synthesized in a systematic review that included only qualitative studies, leading the authors to emphasize the lack of quantitative data in the field (Coleman & Walshe, 2021). To our knowledge, the level of well-being and psychological mechanisms such as deployment of character strengths, emotional management, coping, or the quality of interpersonal relationships, for example, have never been measured in this population. With people living longer and chronic illnesses increasing, the demand for support for those affected by serious and progressive illness is growing (Morris et al., 2013). It is therefore crucial to expand the number of palliative care volunteers, not only by stepping up recruitment, but also by improving ways of retaining them (Pesut et al., 2014). Taking volunteers’ aspirations into account and responding to their difficulties have been identified as major issues, as they contribute to their ongoing involvement (Claxton-Oldfield & Claxton-Oldfield, 2012; Morris et al., 2013; Pesut et al., 2014; Varay et al., 2022). Thus, palliative care volunteers’ well-being requires further attention.

1.1 Well-being

According to the World Health Organization ‘Mental health is a state of mental well-being that enables people to cope with the stresses of life, realize their abilities, learn well and work well, and contribute to their community’ (World Health Organization, 2022). Mental health is a dual concept (Keyes, 2002). Mental well-being is the positive side, psychopathology the negative. Although correlated, these two aspects constitute separate dimensions. Indeed, well-being ranges from flourishing to languishing, irrespective of the presence or absence of mental illness. Mental well-being, or positive mental health (terms used interchangeably), is a multi-faceted construct, comprising emotional, psychological, and social well-being (Ryan & Deci, 2001). It stems from two perspectives. The first, hedonic well-being, focuses on emotional well-being, which reflects emotional vitality. It addresses one’s feelings about life: subjective perception of happiness (i.e., immediate experience of pleasant or unpleasant emotions), satisfaction with life (i.e., one’s long-term evaluation of one’s life), and the balance between positive and negative affects. The second, the eudaimonic current, centers on the individual’s functioning in life. It reflects the psychosocial dimension of well-being. It concentrates on building a person’s capacities and potential, which, when realized, leads to positive functioning in life.

Positive psychology is the orientation of psychology dedicated to well-being. It is defined as ‘the study of the conditions and processes that contribute to the flourishing or optimal functioning of people, groups, and institutions’ (Gable & Haidt, 2005). To improve mental health, a variety of interventions are proposed. While research has abundantly demonstrated the effectiveness of these interventions (Bolier et al., 2013; Sin & Lyubomirsky, 2009), understanding the factors related to mental well-being remains largely unexplored (Rusk & Waters, 2015).

1.2 The SEARCH framework (Waters & Loton, 2019)

The SEARCH framework (Waters & Loton, 2019) was designed to meet this need and overcome limitations of prior models. For example, to mention some of the most popular, the Values in Action Characters Strengths Framework (VIA) (Peterson & Seligman, 2004), has a limited scope as positive psychology has now extended far beyond the field of strengths. The PERMA model (Seligman, 2012, 2018), while considering well-being as a multidimensional concept, is not oriented towards the processes involved—it clarifies the final construct by unpacking it into five end states of well-being: Positive emotion, Engagement, positive Relationships, Meaning and Accomplishment. Finally, the Positive-Activity Model (PAM) (Lyubomirsky & Layous, 2013) describes the psychological mechanisms associated with the effectiveness of positive psychology interventions (emotion, cognition, behaviour and need satisfaction), providing insight into the processes associated with improved well-being, but within a restricted context.

The SEARCH framework (Waters & Loton, 2019) proposes a psychosocial system approach to well-being. Inputs are external factors such as positive psychology interventions, biological or environmental factors. Outputs are well-being outcomes. The internal mechanisms that bridge the gap between inputs and outputs are the processes of positive psychosocial functioning. These processes are categorized into a set of six underlying domains (Strengths, Emotional management, Attention and awareness, Relationships, Coping, and Habits and goals), which were identified through three steps. Firstly, a co-term analysis of more than 18,400 studies related to positive psychology was performed to classify research on positive psychology topics into broad domains (Rusk & Waters, 2015). Next, to test the practical validity of well-being-related themes derived from the literature, a research-action involving educators and students from ten schools was conducted (Waters, 2017). Finally, a systematic review of the literature including 75 intervention studies in school settings confirmed the positive effect of improving each of the pathways corresponding to the identified domains on well-being and academic achievement (Waters & Loton, 2019). This work resulted in the SEARCH model (Waters & Loton, 2019), a data-driven, actionable, evidence-based, multidimensional meta-framework of well-being for guiding research and practice in positive education.

Free to use, it has been applied in numerous schools in Australia, New Zealand, North America, Hong Kong, and the United Arab Emirates, to design, structure, audit or analyze their positive education programs and practices (Waters, 2020). Research, by contrast, seems slow to adopt this model. For example, we have not identified any studies that have operationalized the SEARCH framework (Waters & Loton, 2019) through quantitative questionnaires.

1.3 The Present Study

Given the emotionally demanding setting in which palliative care volunteers participate, there is a need to preserve, support and develop their well-being. To make recommendations, it is worth gaining a better understanding of their mental well-being determinants. The SEARCH framework (Waters & Loton, 2019) offers a conceptual foundation for studying well-being and its related factors, and for developing and assessing well-being-enhancing programs and practices. It was designed in the field of education and, to our knowledge, has never been implemented outside schools. It does, however, seem relevant for exploring well-being in any individual, since it has been used with students, but also with all adult school staff (Waters, 2020).

Thus, this study’s first aim was to ascertain whether well-being factors identified by the SEARCH framework (Waters & Loton, 2019) were associated to well-being in a specific population of palliative care volunteers. The hypothesis was that all the pathways of the SEARCH framework (Waters & Loton, 2019) were positively related to well-being. Then, the second objective of this study was exploratory, it consisted in searching, among the pathways of the SEARCH framework (Waters & Loton, 2019), the determinants of palliative care volunteers’ well-being, i.e. the most appropriate pathways on which to build to improve their well-being.

2 Method

2.1 Design

This study used a subset of data from a larger research project aimed at improving the well-being of palliative care volunteers. Baseline data were here examined to gain deeper insight into the determinants of these volunteers’ well-being. Thus, the present study had a cross-sectional observational design.

2.2 Participants

Participants were palliative care volunteers serving in France. Out of 128 informed consent forms collected, 4 corresponded to subjects who were not eligible, 7 to email addresses given twice and 1 to an invalid email address. As a result, 116 participants were asked to complete the baseline questionnaire. Despite reminders, 14 did not and 1 ended a month after starting, making his data inadmissible as they were potentially biased by the time taken to complete the questionnaire, leaving 101 palliative care volunteers meeting the study’s inclusion criteria (see below).

In this sample, mean age was 64.17 years (SD = 9.57) (see Table 1). Most participants were women (80.2%), living with a partner (65.3%), having pursued higher education (83.2% with 14 years or more), and not professionally active (76.2%).

Table 1 Descriptive data of sociodemographic, well-being, and psychosocial functioning variables
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2.3 Variables and Instruments

2.3.1 Well-being

The Warwick-Edinburgh Mental Well-Being Scale (WEMWBS, Tennant et al., 2007; French version: Trousselard et al., 2016) was used to assess mental well-being. It is a14-item self-reported questionnaire focusing solely on positive attributes of mental health. It reflects a broad conception of well-being, including its core components (affective-emotional aspects, cognitive-evaluative dimensions, and psychological functioning) and covers both hedonic and eudaimonic constructs. Items are rated on a 5-point scale (from 1 = ’none of the time’ to 5 = ’all of the time’). The total score ranges from 14 to 70. A higher score indicates a higher level of mental well-being. Registration to use WEMWBS for non-commercial purposes was completed. Regarding internal consistency, Cronbach’s alpha in this sample was 0.84.

2.3.2 Pathways to Well-being

To evaluate the SEARCH framework pathways (Waters & Loton, 2019), the following self-questionnaires were administered:

  • Strengths were measured by the deployment of character strengths in the activity. In the absence of a validated French-translated instrument, a questionnaire was constructed based on the method used by Littman-Ovadia and Steger (2010). After a brief definition of each of the 24 character strengths from the Inventory of Strengths (Creativity, Curiosity, Judgement, Love of learning, Perspective, Bravery, Perseverance, Honesty, Zest, Love, Kindness, Social intelligence, Teamwork, Fairness, Leadership, Forgiveness, Humility, Prudence, Self-regulation, Appreciation of beauty and excellence, Gratitude, Hope, Humor, and Spirituality) (Peterson & Seligman, 2004), participants indicated on a 5-point scale (from 1 = ’very little’ to 5 = ’very much’) the extent to which they believed they were using each strength in their volunteering. The total score of the 24 items, provided a global measure of character strengths deployment in palliative care volunteer activity, ranging from 24 to 120 (α = 0.83).

  • Emotional Management defined in the SEARCH model as the ability to identify, understand and manage one’s emotions (Waters & Loton, 2019), was evaluated through the Difficulties in Emotion Regulation Scale (DERS, Gratz & Roemer, 2004; French version: Dan-Glauser & Scherer, 2013), which proposes an integrative conceptualization of emotion regulation as involving not just the modulation of emotional arousal, but also the awareness, understanding, and acceptance of emotions, and the ability to act in desired ways regardless of emotional state. This 36-item scale was designed to assess six dimensions of emotion regulation in which difficulties may exist (refusal to accept negative emotions, difficulty engaging in goal-directed behaviors in the presence of negative emotions, difficulty controlling impulsive behaviors in the presence of negative emotions, lack of awareness of emotions, limited access to perceived effective emotion regulation strategies, and lack of understanding of one’s emotions defined as lack of emotional clarity). Items are scored on a 5-point scale (from 1 = ’almost never’ to 5 = ’almost always’). As the aim for this study was to evaluate the effects of emotional management on well-being, only a total DERS scale score (ranging from 36 to 180) was calculated. Lower scores indicate difficulties in emotion regulation. Reliability was high (α = 0.91).

  • Attention and Awareness was measured by the Mindful Attention Awareness Scale (MAAS, Brown & Ryan, 2003; French version: Jermann et al., 2009), assessing the general tendency to be attentive to, and aware of present-moment experience in daily life through 15 items scored on a 6-point scale (from 1 = ’almost always’ to 6 = ’almost never’). The total score ranges from 15 to 90. High scores reflect more present-moment awareness states. In this sample α = 0.89.

  • Relationships were assessed by the Échelle de la Qualité des Relations Interpersonnelles (ÉQRI, Senécal et al., 1992), a 20-item French-language questionnaire designed to measure the quality of interpersonal relationships experienced in different spheres of life: with family, romantic partner, friends, peers, and other people in general. A respondent who has no relationship in one or more spheres does not evaluate it and moves on to the next sphere. Items are rated on a 5-point scale (from 0 = ’not at all’ to 4 = ’extremely’). Scores are calculated for each area separately, ranging from 0 to 16. In this sample α were 0.89; 0,92; 0,88; 0,88; 0,88 respectively.

  • Coping strategies were measured using the Brief Coping Orientation to Problems Experienced Inventory (BriefCope, Carver, 1997; French version: Muller & Spitz, 2003) in its situational format, which assesses the adaptive mechanisms deployed by individuals in response to specific stressful situations. This 28-item, 4-point scale (ranging from 1 = ’I haven’t been doing this at all’ to 4 = ’I’ve been doing this a lot’) explores 14 strategies that can be divided into 4 coping types (Baumstarck et al., 2017). The social support seeking coping type includes venting, emotional support seeking, instrumental support seeking and religion, with scores for the dimension ranging from 8 to 32. The problem solving coping type combines active coping and planning. Scores range from 4 to 16. The avoidance coping type covers behavioral disengagement, self-distraction, substance use, denial and self-blame. Scores range from 10 to 40. Finally, the positive thinking coping type includes humor, positive reframing and acceptance, with scores ranging from 6 to 24. Higher scores reflect a greater tendency to implement the corresponding coping strategy. In this sample Cronbach’s alpha coefficients were acceptable, except for the avoidance factor (0.77; 0,73; 0,48; 0,65 respectively). In the Baumstarck et al. (2017) study, this factor also had the lowest alpha (0.82; 0.74; 0.64; 0.71 respectively).

  • Habits and Goals driven actions were evaluated by the Shortened Committed Action Questionnaire (CAQ-8, McCracken et al., 2015; French version: Gagnon et al., 2017). This 8-item questionnaire, rated on a 7-point scale (from 0 = ’never true’ to 6 = ’always true’), was designed to assess the tendency to persist in goal-directed behaviors related to values. The total score ranges from 0 to 48. A high score indicates a high level of committed action (action that is guided by goals and values). In this sample α was 0.68.

Sociodemographic data were also collected (age, gender, marital status, years of education and professional status).

2.4 Procedure

The entire procedure was online. Participants were recruited between March and June 2022. An email inviting participation, with a link to the study, was widely posted to federations and associations of palliative care volunteers in France, asking them to pass it on to their members. Volunteers in contact with the first author through previous studies were also approached directly. Inclusion criteria were to be aged 18 or over, involved as a palliative care volunteer in France, fluent in French, having internet access and an email address. Those who chose to click the link were directed to a secure platform. A letter presented the objectives and procedures of the study, specifying they were free to participate, to withdraw at any time, and that their anonymity would be respected. To enable them to ask any questions, contact details were provided. They could give informed consent by ticking a box. Eligibility was then checked, before a link to baseline assessment including well-being, SEARCH pathways (Strengths, Emotional management, Attention and awareness, Relationships, Coping, and Habits and goals, Waters & Loton, 2019) and sociodemographic data was transmitted. Responses were mandatory to progress through the questionnaire. Participants who had not fully completed the assessment within a week from the start to the end of the questionnaire were excluded to ensure consistency of the data.

2.5 Ethics

Ethical approval was obtained for the overall project from the ethics committee at the university where the authors are currently affiliated (Comité d’Éthique de la Recherche Université de Paris, France, N° IRB: 00012021–79, 12.10.2021), and the latest version of the Declaration of Helsinki was respected at all times. All participants provided written informed consent.

2.6 Statistical Analysis

Analysis consisted of descriptive statistics (percentage, mean, standard deviation) to illustrate population characteristics. Bivariate analysis enabled to explore the relations between sociodemographic and psychosocial functioning variables, and outcome measure of well-being. Normal distribution of data for each continuous variable was evaluated using the Kolmogorov–Smirnov test. Given the results, parametric tests were performed to assess the association between well-being and age (Pearson’s correlation), gender, marital status and professional status (Student’s t), and years of education (ANOVA). Non-parametric tests were chosen to analyze the correlation between well-being and psychosocial variables (Spearman correlation). Finally, multiple linear regression allowed to investigate the ability of psychosocial functioning factors to predict levels of well-being. Considering the exploratory nature of this objective, a stepwise approach that selects independent variables according to their relevance to the model was retained. With this method, the first variable is chosen on the basis of the highest correlation, and subsequent variables on the basis of partial correlation. When a variable is added to the model, it is assessed not only whether it makes a significant contribution, but also whether the variable that contributed least to the model remains significant. If not, it is removed. In this way, redundant variables can be eliminated. To ensure the data were sufficiently correlated with the dependent variable for examination through multiple linear regression to be reliably performed, sociodemographic and psychosocial functioning variables were included as independent variables at the following conditions: be significantly related to well-being for categorical variables, combined with a correlation coefficient | r |> 0.30 for continuous variables. Before running multiple regression, all assumptions were checked, and no violations were found. The significance level was set at 0.05 for all analyses. It was expected to collect at least 100 responses to ensure satisfactory statistical power. Data were analyzed with IBM SPSS 20 software package.

3 Results

The final sample size was n = 101. Regression analyses were carried out on 75 of the 101 participants, as some were not concerned by all dimensions of the relationship scale, resulting in missing data.

Mean and standard deviation of the measured well-being and psychosocial functioning variables are presented in Table 1.

None of the sociodemographic variables (age, gender, marital status, years of education, professional status) was significantly associated with well-being (p = 0.88, 0.26, 0.07, 0.64 and 0.73 respectively).

Correlations between well-being and psychosocial functioning variables are described in Table 2. Among the thirteen different aspects of psychosocial functioning, ten (strengths deployment, emotional management difficulties, attention and awareness, relationships with family, love partner, friends, and people in general, coping based on social support seeking, avoidance, and positive thinking), revealed correlations with well-being that were significant and in the expected direction (respectively r = 0.27, p < 0.01; r = -0.40, p < 0.01; r = 0.24, p < 0.05; r = 0.40, p < 0.01; r = 0.41, p < 0.01; r = 0.26, p < 0.01; r = 0.40, p < 0.01; r = 0.33, p < 0.01; r = -0.29, p < 0.01; and r = 0.22, p < 0.05). Three (relationships with peers, coping based on problem solving, and actions taken in relation to habits and goals), did not show significant correlations with well-being (respectively r = 0.12, p = 0.223; r = 0.11, p = 0.263 and r = 0.19, p = 0.061).

Table 2 Correlations between well-being and psychosocial functioning variables
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A stepwise multiple linear regression with well-being as the dependent variable and the five aspects of psychosocial functioning significantly associated with well-being with | r |> 0.30 as independent variables (emotional management difficulties, relationships with family, love partner, and people in general, and coping based on social support seeking) was run. All statistical assumptions of multiple linear regression were met. Normality, linearity, and homoscedasticity were checked through histogram and scatterplots. There was no multicollinearity among the independent variables, as they did not correlate highly with each other (see Table 2). In addition, all tolerance scores were > 0.89 and VIF values < 1.4. Regarding independence of residuals, the Durbin-Watson test value (2.16) suggested no autocorrelation between model prediction errors. Results showed that of the five independent variables, only three (emotional management difficulties, relationships with family, and coping based on social support seeking) were upheld as significant predictors that in combination contributed significantly to well-being of palliative care volunteers (F (3, 71) = 12.73, p < 0.001) (Tables 3 and 4). Multiple correlation was found to be R = 0.59 for emotional management difficulties, relationships with family, and coping based on social support seeking which accounted for 35% variance well-being scores. Emotional management difficulties exerted a significant negative influence (β = -0.40; p < 0.001) on well-being of palliative care volunteers and contributed about 22% in their well-being (ΔR2 = 0.22, F (1, 73) = 20.14; p < 0.001). This indicates that the increase in emotional management difficulties of palliative care volunteers leads to the decrease in their well-being. Relationships with family emerged as the next significant potential predictor (β = 0.25; p < 0.05) of well-being of palliative care volunteers which contributed approximately 8% (ΔR2 = 0.08, F (1, 72) = 8.68; p < 0.01) of variance in their well-being. It means that when the quality of palliative care volunteers’ relationships with their families improves, so does their well-being. Lastly a type of coping based on social support seeking strategies also increases the variance by 5% making the prediction to improve further in a significant manner (ΔR2 = 0.05, F (1, 71) = 5.37; p < 0.05) and exerted a significant positive influence (β = 0.23; p < 0.05) on well-being of palliative care volunteers, indicating that as the palliative care volunteers’ coping based on social support seeking improves, their well-being will improve too.

Table 3 Summary for the predictive Models of Palliative Care Volunteers’ Well-being (n = 75)
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Table 4 Stepwise Multiple Linear Regression Analysis for Psychosocial Functioning Predicting Well-being in Palliative Care Volunteers (n = 75)
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4 Discussion

This study examined determinants of well-being among palliative care volunteers, using the SEARCH framework (Waters & Loton, 2019) from the field of positive education to identify them. This model proved transferable, as almost all the pathways proposed were positively related to volunteers’ well-being: strengths, emotional management (since emotional management difficulties negatively impacted well-being), attention and awareness, relationships (with family, romantic partner, friends, and people in general), coping (based on social support-seeking, avoidance, and positive thinking). Only the habits and goals pathway was not associated with palliative care volunteers’ well-being, as well as, in the relationships pathway, those with peers, and in the coping pathway, that based on problem-solving. In this specific population, the determinants of well-being, i.e., the most effective pathways for improving well-being appeared to be emotional management, relationships with family, and coping based on seeking social support.

The SEARCH framework (Waters & Loton, 2019) was developed for school settings. Its rigorous methodological design and its use in practice with people of all ages, students and adults accompanying them (Waters, 2020), led to the assumption it could be generalized. By transposing it, for the first time to our knowledge, to another field, and by showing its relevance to investigate well-being and its psychological pathways among palliative care volunteers, this study provided a first clue.

The exploration of well-being determinants in this population revealed that, of the six pathways proposed by the SEARCH framework (Waters & Loton, 2019), three emerged as more important: emotional management, relationships with family, and coping based on seeking social support, which together accounted for 35% of palliative care volunteers’ well-being.

Emotional management had the greatest influence, contributing 22% to palliative care volunteers’ well-being. This result can be explained by the context of the activity, at risk of fragilization (Hulbert & Morrison, 2006). In palliative care, volunteers were shown to be exposed to the same stressors as paid staff (Coleman & Walshe, 2021). For staff, emotional distress was seen as a manifestation of occupational stress that had to be managed (Yancik, 1984). Volunteers did report that, to cope with their difficulties, they developed emotional regulation strategies, such as keeping sufficient emotional distance from patients, looking at the positive in every situation, or making sense (Morris et al., 2013). They expressed a desire for more information and training to help them face their mission, for example around the topics of death, or managing emotions (Coleman et al., 2022; Morris et al., 2013). Therefore, given the sources of stress, coping strategies used, and needs of palliative care volunteers stemming from the literature, it is not surprising emotional management was found a pathway to volunteer well-being.

Relationships with their families emerged as the second pathway to well-being for palliative care volunteers, accounting for 8%. Thus, among all interpersonal relationships, the family circle appeared as a privileged place for resourcing. Considering the well-documented profile of palliative care volunteers: mostly married, middle-aged, financially comfortable, unemployed, female, with some college education and strong religious beliefs (Starnes & Wymer, 2000), in line with the sociodemographic data collected in this study, this determinant of well-being might be more closely rooted in the personal characteristics of volunteers than in the characteristics of the activity.

Thirdly, developing coping strategies based on seeking social support was the last significant pathway to well-being for palliative care volunteers. This factor had a 5% share of variance in volunteers’ well-being. This result complements the previous one. It underlines the importance of relationships for volunteers. Literature relating to palliative care staff (which can be extended to volunteers involved in the field, Coleman & Walshe, 2021) indicated that stress was increased when mechanisms such as social support were not available (Vachon, 1995). Volunteers mentioned interactions could be difficult and suffering from a lack of understanding, consideration, and support (Pesut et al., 2014). They reported a need for better communication and integration with the healthcare team (Laperle & Ummel, 2019). Investigations focused on palliative care volunteers’ coping showed they mobilized not only internal resources such as emotional regulation mentioned above, but also, external resources based on social support-seeking, such as soliciting advice from medical staff, talking with other volunteers, or, more generally, other people (Morris et al., 2013). Findings from this study suggest social support-seeking is an appropriate response for palliative care volunteers to cope with stress and improve well-being.

The strength of this research lies in expanding insights into the little-studied topic of psychosocial factors determining well-being. Transferring the SEARCH framework (Waters & Loton, 2019) from the field of education to that of palliative care, provides a first indication of the generalizability of this recent model of well-being pathways. By identifying the most important pathways to well-being in the specific population of palliative care volunteers, this study offers practical guidelines for training, supervision, and support of these volunteers, and ultimately for their retention.

Yet this study presents some limitations. Firstly, participants were self-selected, representing a possible recruitment bias. The theme of the study may have been particularly attractive to a certain type of person, and the design did not allow for analysis of the profile of non-respondents. These factors may have undermined the representativeness of the sample and the generalizability of the results. Secondly, stepwise regressions, recommended for exploratory research (Menard, 2002), are sensitive to sampling errors, which may impact on the replicability of conclusions (Thompson, 1995). The selection of independent variables is determined at each stage on the basis of their partial correlation with the dependent variable. Sometimes, the choice of one variable over another is based on a very small difference, and this choice has an impact on all subsequent selections. This risk is reduced, however, when the sample size is large and the number of predictors small, which is why it was decided to retain only those independent variables presenting a correlation rate with the independent variable | r |> 0.30.

In sum, the results of this study indicated the main pathways to well-being for palliative care volunteers according to the SEARCH framework (Waters & Loton, 2019): emotional management, relationships with family, and coping based on social support-seeking. These pathways figure among the flexible and varied coping strategies deployed by volunteers, who reported using emotional management and social support-seeking (Morris et al., 2013). To address occupational challenges (suffering related to the end-of-life context, stress-generating interactions, lack of support, Coleman et al., 2022), and volunteer needs (information, training, improved communication, emotional support, integration into the care team and recognition, Laperle & Ummel, 2019; Morris et al., 2013), targeted actions aimed at cultivating these three pathways to well-being should help volunteers not only to face adversity but also to flourish. It should be noted, however, that most studies on the emotional experience of palliative care volunteers were conducted on small samples, drawn from Anglo-Saxon populations, and were based on descriptive or qualitative methodology, more rarely quantitative or mixed (Morris et al., 2013; Pesut et al., 2014; Varay et al., 2022). This research builds on previous knowledge by grounding the exploration of well-being and its psychological pathways among palliative care volunteers in a modelling approach, rooted in the theoretical referential of positive psychology, and by using a quantitative methodology. The results showed, a priori for the first time, how well three psychosocial variables together predicted their well-being, and determined the strength of the relation each predictor separately had with well-being. By suggesting the SEARCH framework (Waters & Loton, 2019) was appropriate for bringing well-being into the palliative care setting, this preliminary investigation offers new avenues for research and practice. Indeed, given the growing need for volunteers in palliative care, and the recommendations in the literature indicating that taking care of volunteers, by listening, supervising, and supporting them, was key to sustaining their involvement (Coleman et al., 2022), a meta-framework can help the field of palliative care to build cumulative evidence needed to move forward in studying and understanding volunteers’ well-being. It can also provide a frame to embed well-being concern in a consistent direction, leading to the development of structured practices, programs, and policies to help managers, volunteer coordinators and service planners to alleviate volunteers’ occupational stress and to support them appropriately. These actions could be inspired by those proposed in the school area when applying the SEARCH framework (Waters & Loton, 2019). In this environment, two types of interventions were deployed to improve well-being through emotional management: the first targeted emotional intelligence (learning to develop the ability to perceive, understand, use, and regulate emotions), and the second targeted gratitude (aimed at better noticing, appreciating, and recognizing the positive). The relationship pathway was activated by mentoring interventions (the more experienced looking after, guiding, and supporting the less experienced, to strengthen interpersonal bonds). And the coping pathway through interventions directly addressing coping (learning to change thoughts and behaviours adopted in response to stress) or resilience (developing the ability to face adversity). In school populations, evidence of the effectiveness of these interventions in increasing well-being through the different pathways was provided as part of developing the SEARCH framework (Waters & Loton, 2019). Future research should confirm that these interventions are also effective in boosting the pathways and final well-being of palliative care volunteers.

5 Conclusions

This study contributed to the understanding of the pathways to well-being by showing that the SEARCH framework (Waters & Loton, 2019), developed in the field of education, was transferable to other sectors, and that among palliative care volunteers the most important pathways to well-being were emotional management, relationships with family, and coping based on seeking social support. Pursuing the use of this scientifically established, actionable, and free-of-charge framework in the palliative care setting should enable building rigorous research and reliable practices to support volunteers’ well-being. At a time of increasing demand for palliative care support, this perspective meets a crucial necessity. Future research is needed to confirm the generalizability of the SEARCH framework (Waters & Loton, 2019), and the practical validity of the proposed school-based interventions to improve well-being across each of the pathways in other populations.