Predicting failure before it happens: A 5-year, 1042 participant prospective study.


Journal

Medical teacher
ISSN: 1466-187X
Titre abrégé: Med Teach
Pays: England
ID NLM: 7909593

Informations de publication

Date de publication:
09 2021
Historique:
pubmed: 13 4 2021
medline: 1 4 2022
entrez: 12 4 2021
Statut: ppublish

Résumé

Students who fail assessments are at risk of negative consequences, including emotional distress and cessation of studies. Identifying students at risk of failure before they experience difficulties may considerably improve their outcomes. Using a prospective design, we collected simple measures of engagement (formative assessment scores, compliance with routine administrative tasks, and attendance) over the first 6 weeks of Year 1. These measures were combined to form an engagement score which was used to predict a summative examination sat 14 weeks after the start of medical school. The project was repeated for five cohorts, giving a total sample size of 1042. Simple linear regression showed engagement predicted performance ( At-risk medical students can be identified with some accuracy immediately after starting medical school using routinely collected, easily analysed data, allowing for tailored interventions to support students. The toolkit provided here can reproduce the predictive model in any equivalent educational context. Medical educationalists must evaluate how the advantages of early detection are balanced against the potential invasiveness of using student data.

Identifiants

pubmed: 33844612
doi: 10.1080/0142159X.2021.1908526
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1039-1043

Commentaires et corrections

Type : CommentIn

Auteurs

Avril Dewar (A)

Edinburgh Medical School, University of Edinburgh, Edinburgh, UK.

David Hope (D)

Edinburgh Medical School, University of Edinburgh, Edinburgh, UK.

Alan Jaap (A)

Edinburgh Medical School, University of Edinburgh, Edinburgh, UK.

Helen Cameron (H)

Aston Medical School, Aston University, Birmingham, UK.

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Classifications MeSH