The Need to Understand Medical Student-Specific Validity of Well-Being Scales
Notice bibliographique
Résumé
To the Editor: Bynum and colleagues1 reported on the systematic development of the Shame Frequency Questionnaire in Medical Students. We agree there is a need for well-being measures with validity evidence in medical students. Furthermore, we suggest that convergent validity is best established using reference measures that have been validated in medical students. In their article, the authors use the 10-item Center for Epidemiological Studies Depression Scale (CES-D-10) as a measure of depression. This scale was originally developed for use in older adults2 and does not seem to have been validated in the medical student population. Therefore, if we are unsure that the CES-D-10 accurately reflects depression in medical students, how confident can we be that correlation with the CES-D-10 supports the validity of another scale? We suggest that validity evidence for the Shame Frequency Questionnaire in Medical Students could be strengthened by comparing it to the 20-item CES-D3 or the Patient Health Questionnaire-9 (PHQ-9),4 which have been validated in medical students as accurate surrogate measures of diagnostic interviews. As Bynum and colleagues correctly suggest, using existing scales from other contexts without validity evidence to support their intended use in medical students impairs our ability to understand phenomena in undergraduate medical education. Thorough investigations of medical student well-being scale validity are therefore necessary to not only support the selection of measures but also provide trustworthy reference points for use in the development and validation of new scales that may measure different constructs. Given the wide array of scales currently being used to measure medical student well-being,5 it would be inconceivable to pursue complete evaluations of every such scale. We suggest that a robust synthesis of existing validity evidence could help identify scales for each construct (i.e., depression, burnout, etc.) that hold promise for further use and validation. In addition, consensus-based activities with key stakeholders, including learners, could highlight important constructs to measure as part of medical student well-being. Ensuring that we use consistent and psychometrically sound tools can enhance our efforts to advance well-being in medical education and ultimately the delivery of health care. Henry Li, MDResident, Department of Emergency Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada; email: [email protected]; X (formerly Twitter): @HenryLiCDN; ORCID: https://orcid.org/0000-0002-1594-347XVictor Do, MD, MScClinical assistant professor, Department of Pediatrics, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, CanadaAliya Kassam, MSc, PhDAssociate professor, Department of Community Health Sciences and Office of Postgraduate Medical Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; ORCID: https://orcid.org/0000-0002-7081-6377
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,086 | 0,438 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,008 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,006 | 0,003 |
| Intégrité de la recherche | 0,017 | 0,027 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».