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A Qualitative and Semiquantitative Exploration of the Experience of a Rural and Regional Clinical Placement Programme

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Abstract

In many countries, including New Zealand, recruitment of medical practitioners to rural and regional areas is a government priority, yet evidence for what determines career choice remains limited. We studied 19 newly qualified medical practitioners, all of whom had participated in a year-long undergraduate rural or regional placement (the Pūkawakawa Programme). We explored their placement experiences through focus groups and interviews and aimed to determine whether experiential differences existed between those who chose to return to a rural or regional location for early career employment (the Returners) and those who did not (the Non-Returners). Focus group and interview transcripts were a mean (range) length of 6485 (4720–7889) and 3084 (1843–4756) words, respectively, and underwent thematic analysis. We then used semiquantitative analysis to determine the relative dominance of themes and subthemes within our thematic results. Placement experiences were overwhelming positive – only four themes emerged for negative experiences, but five themes and nine subthemes emerged for positive experiences. Many curricular aspects of the placement experience were viewed as similarly positive for Returners and Non-Returners, as were social aspects with fellow students. Hence, positive experiences per se appear not to differentiate Returner and Non-Returner groups and so seem unlikely to be related to decisions about practice location. However, Returners reported a substantially higher proportion of positive placement experiences related to feeling part of the clinical team compared with Non-Returners (11% vs 4%, respectively) – a result consistent with Returners also reporting more positive experiences related to learning and knowledge gained and personal development.

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References

  1. Gill D, Palmer C, Mulder R, Wilkinson T. Medical student career intentions at the Christchurch School of Medicine. The New Zealand Wellbeing, Intentions, Debt and Experiences (WIDE) survey of medical students pilot study. Results Part II. N Z Med J. 2001;114:465–7.

    Google Scholar 

  2. Lent RW, Brown SD, Talleyrand R, et al. Career choice barriers, supports, and coping strategies: college students’ experiences. J Vocat Behav. 2002;60:61–72.

    Article  Google Scholar 

  3. Webster CS, Ling C, Barrow M, Poole P, Henning M. A cross-disciplinary assessment of student loans debt, financial support for study and career preferences upon graduation. N Z Med J. 2017;130:43–53.

    Google Scholar 

  4. Ling S, Jacobs R, Ponton R, et al. Influence of student debt on health career location and specialty. J Prim Health Care. 2018;10:54–61.

    Article  Google Scholar 

  5. Poole P, Bourke D, Shulruf B. Increasing medical student interest in general practice in New Zealand: where to from here? N Z Med J. 2010;123:12–9.

    Google Scholar 

  6. Bale AG, Coutinho K, Swan KG, Heintich GF. Increasing educational indebtedness influences medical students to pursue specialization: a military recruitment potential? Mil Med. 2013;178:202–6.

    Article  Google Scholar 

  7. Phillips JP, Weismantel DP, Gold KJ, Schwenk TL. Medical student debt and primary care specialty intentions. Fam Med. 2010;42:616–22.

    Google Scholar 

  8. Pisaniello MS, Asahina AT, Bacchi S, et al. Effect of medical student debt on mental health, academic performance and specialty choice: a systematic review. BMJ Open. 2019;9:e029980. https://doi.org/10.1136/bmjopen-2019-029980.

    Article  Google Scholar 

  9. McKillop A, Webster CS, Bennett W, O'Connor B, Bagg W. Encouragers and discouragers affecting medical graduates’ choice of regional and rural practice locations. Rural Remote Health. 2017;17:4247. Available from:. https://doi.org/10.22605/RRH4247.

    Article  Google Scholar 

  10. London MT, Burton JG. Grasping the Ongaonga: when will New Zealand really integrate rural clinical education? Aust J Rural Health. 2018;26:323–8.

    Article  Google Scholar 

  11. Poole P, Bagg W, O'Connor B, et al. The Northland Regional-Rural program (Pūkawakawa): broadening medical undergraduate learning in New Zealand. Rural Remote Health. 2010;10:1254 Available from: http://www.rrh.org.au/journal/article/1254. Accessed 15 Mar 2020.

  12. Walker JH, DeWitt DE, Pallant JF, Cunningham CE. Rural origin plus a rural clinical school placement is a significant predictor of medical students’ intentions to practice rurally: a multi-university study. Rural Remote Health. 2012;12:1908 Available from: https://rrh.org.au/journal/article/1908. Accessed 15 Mar 2020.

  13. Sen Gupta T, Woolley T, Murray R, Hays R, McCloskey T. Positive impacts on rural and regional workforce from the first seven cohorts of James Cook University medical graduates. Rural Remote Health. 2014;14:2657 Available from: http://rrh.org.au/journal/article/2657. Accessed 15 Mar 2020.

  14. Jamar E, Newbury J, Mills D. Early career location of University of Adelaide rural cohort medical students. Rural Remote Health. 2014;14:2592 Available from: https://rrh.org.au/journal/article/2592. Accessed 15 Mar 2020.

  15. Isaac V, Watts L, Forster L, McLachlan CS. The influence of rural clinical school experiences on medical students’ levels of interest in rural careers. Hum Resour Health. 2014;12:48 Available from: http://www.human-resources-health.com/content/12/1/48. Accessed 15 Mar 2020.

  16. McMichael AJ. Prisoners of the proximate: loosening the constraints on epidemiology in an age of change. Am J Epidemiol. 1999;149:887–97.

    Article  Google Scholar 

  17. Gorman D. Matching the production of doctors with national needs. Med Educ. 2018;52:103–13.

    Article  Google Scholar 

  18. Francis J, Johnston M, Robertson C, et al. What is an adequate sample size? Operationalising data saturation for theory-based interview studies. Psychol Health. 2010;25:1229–45.

    Article  Google Scholar 

  19. Thomas DR. A general inductive approach for analysing qualitative evaluation data. Am J Eval. 2006;27:237–46.

    Article  Google Scholar 

  20. Bowen GA. Grounded theory and sensitizing concepts. Int J Qual Methods. 2006;5:12–23.

    Article  Google Scholar 

  21. Bhimavarapu KR, Doerr WW. A semiquantitative risk assessment methodology to prioritize recommendations. Process Saf Prog. 2009;28:356–61.

    Article  Google Scholar 

  22. Kostoff RN. Semiquantitative methods for research impact assessment. Technol Forecast Soc Change. 1993;44:231–44.

    Article  Google Scholar 

  23. Gorman D. Seven steps to redistributing doctors to meet health needs better. Intern Med J. 2017;47:845–7.

    Article  Google Scholar 

  24. Gibis B, Heinz A, Jacob R, Müller CH. The career expectations of medical students: findings of a nationwide survey in Germany. Dtsch Arztebl Int. 2012;109:327–32.

    Google Scholar 

  25. Thapa KR, Shrestha BK, Bhattarai MD. Study of working experience in remote rural areas after medical graduation. Kathmandu Univ Med J. 2014;46:121–5.

    Google Scholar 

  26. Ebuehi OM, Campbell PC. Attraction and retention of qualified health workers to rural areas in Nigeria: a case study of four LGAs in Ogun State, Nigeria. Rural Remote Health. 2011;11:1515 Available from: https://www.rrh.org.au/journal/article/1515. Accessed 15 Mar 2020.

  27. Wang J, Su J, Zuo H, Jia M, Zeng Z. What interventions do rural doctors think will increase recruitment in rural areas: a survey of 2778 health workers in Beijing. Hum Resour Health. 2013;11:40 Available from: http://www.human-resources-health.com/content/11/1/40. Accessed 15 Mar 2020.

  28. Merry AF, Davies JM, Maltby JR. Qualitative research in health care. Br J Anaesth. 2000;84:552–5.

    Google Scholar 

  29. Hollnagel E. FRAM: The Functional Resonance Analysis Method - Modelling Complex Socio-technical Systems. London: CRC Press; 2012.

    Google Scholar 

  30. de Carvalho PVR. The use of Functional Resonance Analysis Method (FRAM) in a mid-air collision to understand some characteristics of the air traffic management system resilience. Reliab Eng Syst Saf. 2011;96:1482–98.

    Article  Google Scholar 

  31. Bergman MM. Advances in mixed methods research: theories and applications. London: Sage; 2008.

    Book  Google Scholar 

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Acknowledgements

The authors would like to thank Dr. Lyn Lavery and the Academic Consulting, Auckland, New Zealand, for conducting the thematic analysis of our data.

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Correspondence to Craig S. Webster.

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Ethics approval was gained from the University of Auckland Human Participants Ethics Committee (Ref. 9890), and locality approval obtained from the Northland District Health Board.

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Webster, C.S., McKillop, A., Bennett, W. et al. A Qualitative and Semiquantitative Exploration of the Experience of a Rural and Regional Clinical Placement Programme. Med.Sci.Educ. 30, 783–789 (2020). https://doi.org/10.1007/s40670-020-00949-6

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