Reporting-bias in Surveys of Sensitive Personal Information
Notice bibliographique
Résumé
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.
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 | 0,348 | 0,666 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,008 |
| Communication savante | 0,005 | 0,007 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,014 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,004 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».