The Need to Understand Medical Student-Specific Validity of Well-Being Scales
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.438 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".