Reporting-bias in Surveys of Sensitive Personal Information
Bibliographic record
Abstract
In Reply: We agree with Dr. Larson that alcohol and drug use may be more prevalent among residents than we could identify. Despite a respectable 71% response rate, residents with greater substance use may have been reluctant to participate. Although anonymity was assured, some residents may have felt wary about endorsing certain issues. Such nonresponse and social desirability biases are limitations of all surveys, especially when sensitive questions are posed. Whether medical trainees have similar rates of alcohol and drug use compared to their age group is not entirely clear. A survey of Minnesota medical students, which had a 50% response rate, reported that 15% and 22% of respondents met criteria for binge drinking and at-risk alcohol use, respectively.1 As Dr. Larson correctly notes, trainees and medical professionals may minimize or deny health concerns, including substance use or other issues. That over one-quarter of respondents in our study endorsed “some concern” to “great concern” in the past year with depression, anxiety, or relationship problems suggests that residents did not simply deny all potentially stigmatizing health-related concerns. Moreover, the question about jeopardy to training status was framed hypothetically, that is, “How concerned would you be that your training status or future professional opportunities might be jeopardized if your residency training director or your clinical supervisor learned that you had a current problem with…?” Therefore, the overall stronger levels of concern about jeopardy to training status from certain health issues versus others may have been driven more by perceived stigma associated with those issues than by personal concern or experience with those issues. As reported in a previous article,2 our survey also uncovered other findings related to stigma. Specifically, the survey presented vignettes about residents with specific health issues, with items addressing potential stigma and fears of professional jeopardy. Responses to these vignettes revealed that residents indicated greater likelihood of avoiding care at their training institution, and greater concern about training status, when the vignette described a resident with alcohol abuse than when it depicted a resident with diabetes. We applaud Dr. Larson for highlighting these “shadow issues”—trainee substance use, stigma associated with sensitive health issues and behaviors, and difficulties in assessing these concerns via surveys. We share the hope that residents' personal health needs—including access to appropriate and confidential evaluation and treatment for mental health and substance use issues—will receive greater attention. Trainees deserve no less. Laura B. Dunn, MD Associate professor, Department of Psychiatry, Director of Psycho-Oncology, University of California, San Francisco, San Francisco, California; [email protected]. Laura W. Roberts, MD, MA Charles E. Kubly Professor and Chairman, Department of Psychiatry and Behavioral Medicine, and professor of bioethics, Department of Population Health, Medical College of Wisconsin, Milwaukee, Wisconsin.
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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 | 0.348 | 0.666 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".