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Sample representativeness and influence of attrition on longitudinal data collected as part of a national medical career tracking project

BACKGROUND: The Medical Schools Outcomes Database and Longitudinal Tracking Project (MSOD) in New Zealand is one example of a national survey-based resource of medical student experiences and career outcomes. Longitudinal studies of medical students are valuable for evaluating the outcomes of medica...

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Autores principales: Connell, Charlotte J. W., Salkeld, Alexander J, Wells, Cameron, Verstappen, Antonia C., Poole, Phillippa, Wilkinson, Tim J, Bagg, Warwick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369717/
https://www.ncbi.nlm.nih.gov/pubmed/37491266
http://dx.doi.org/10.1186/s12909-023-04472-1
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author Connell, Charlotte J. W.
Salkeld, Alexander J
Wells, Cameron
Verstappen, Antonia C.
Poole, Phillippa
Wilkinson, Tim J
Bagg, Warwick
author_facet Connell, Charlotte J. W.
Salkeld, Alexander J
Wells, Cameron
Verstappen, Antonia C.
Poole, Phillippa
Wilkinson, Tim J
Bagg, Warwick
author_sort Connell, Charlotte J. W.
collection PubMed
description BACKGROUND: The Medical Schools Outcomes Database and Longitudinal Tracking Project (MSOD) in New Zealand is one example of a national survey-based resource of medical student experiences and career outcomes. Longitudinal studies of medical students are valuable for evaluating the outcomes of medical programs against workforce objectives. As a prospective longitudinal multiple-cohort study, survey response rates at each collection point of MSOD vary. This paper assesses the effects of participant non-response rates on MSOD data. METHODS: Demographic variables of MSOD respondents between 2012 and 2018 were compared to the distribution of the demographic variables in the population of all NZ medical graduates to ascertain whether respondent samples at multiple survey collection points were representative of the population. Analysis using logistic regression assessed the impact of participant non-response on variables at collection points throughout MSOD. RESULTS: 2874 out of a total population of 2939 domestic medical students graduating between 2012 and 2018 responded to MSOD surveys. Entry and exit surveys achieved response rates around 80% and were broadly representative of the total population on demographic variables. Post-graduation survey response rates were around 50% of the total population of graduates and underrepresented graduates from the University of Auckland. Between the entry and exit and the exit and postgraduation year three samples, there was a significant impact of non-response on ascribed variables, including age at graduation, university, gender and ethnic identity. Between the exit and postgraduation year one sample, non-response significantly impacted ascribed and non-ascribed variables, including future practice intentions. CONCLUSION: Samples collected from MSOD at entry and exit are representative, and findings from cross-sectional studies using these datasets are likely generalisable to the wider population of NZ medical graduates. Samples collected one and three years post-graduation are less representative. Researchers should be aware of this bias when utilizing these data. When using MSOD data in a longitudinal manner, e.g. comparing the change in career intentions from one collection point to the next, researchers should appropriately control for bias due to non-response between collection points. This study highlights the value of longitudinal career-tracking studies for answering questions relevant to medical education and workforce development.
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spelling pubmed-103697172023-07-27 Sample representativeness and influence of attrition on longitudinal data collected as part of a national medical career tracking project Connell, Charlotte J. W. Salkeld, Alexander J Wells, Cameron Verstappen, Antonia C. Poole, Phillippa Wilkinson, Tim J Bagg, Warwick BMC Med Educ Research BACKGROUND: The Medical Schools Outcomes Database and Longitudinal Tracking Project (MSOD) in New Zealand is one example of a national survey-based resource of medical student experiences and career outcomes. Longitudinal studies of medical students are valuable for evaluating the outcomes of medical programs against workforce objectives. As a prospective longitudinal multiple-cohort study, survey response rates at each collection point of MSOD vary. This paper assesses the effects of participant non-response rates on MSOD data. METHODS: Demographic variables of MSOD respondents between 2012 and 2018 were compared to the distribution of the demographic variables in the population of all NZ medical graduates to ascertain whether respondent samples at multiple survey collection points were representative of the population. Analysis using logistic regression assessed the impact of participant non-response on variables at collection points throughout MSOD. RESULTS: 2874 out of a total population of 2939 domestic medical students graduating between 2012 and 2018 responded to MSOD surveys. Entry and exit surveys achieved response rates around 80% and were broadly representative of the total population on demographic variables. Post-graduation survey response rates were around 50% of the total population of graduates and underrepresented graduates from the University of Auckland. Between the entry and exit and the exit and postgraduation year three samples, there was a significant impact of non-response on ascribed variables, including age at graduation, university, gender and ethnic identity. Between the exit and postgraduation year one sample, non-response significantly impacted ascribed and non-ascribed variables, including future practice intentions. CONCLUSION: Samples collected from MSOD at entry and exit are representative, and findings from cross-sectional studies using these datasets are likely generalisable to the wider population of NZ medical graduates. Samples collected one and three years post-graduation are less representative. Researchers should be aware of this bias when utilizing these data. When using MSOD data in a longitudinal manner, e.g. comparing the change in career intentions from one collection point to the next, researchers should appropriately control for bias due to non-response between collection points. This study highlights the value of longitudinal career-tracking studies for answering questions relevant to medical education and workforce development. BioMed Central 2023-07-25 /pmc/articles/PMC10369717/ /pubmed/37491266 http://dx.doi.org/10.1186/s12909-023-04472-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Connell, Charlotte J. W.
Salkeld, Alexander J
Wells, Cameron
Verstappen, Antonia C.
Poole, Phillippa
Wilkinson, Tim J
Bagg, Warwick
Sample representativeness and influence of attrition on longitudinal data collected as part of a national medical career tracking project
title Sample representativeness and influence of attrition on longitudinal data collected as part of a national medical career tracking project
title_full Sample representativeness and influence of attrition on longitudinal data collected as part of a national medical career tracking project
title_fullStr Sample representativeness and influence of attrition on longitudinal data collected as part of a national medical career tracking project
title_full_unstemmed Sample representativeness and influence of attrition on longitudinal data collected as part of a national medical career tracking project
title_short Sample representativeness and influence of attrition on longitudinal data collected as part of a national medical career tracking project
title_sort sample representativeness and influence of attrition on longitudinal data collected as part of a national medical career tracking project
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369717/
https://www.ncbi.nlm.nih.gov/pubmed/37491266
http://dx.doi.org/10.1186/s12909-023-04472-1
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