Developing a Framework for Mental Health Disclosure Decision-Making Among Medical Students: A Qualitative Pilot Study
Bibliographic record
Abstract
Purpose American medical students exhibit higher rates of depression, anxiety, and psychological distress than age-matched members of the general population, yet the majority of students do not seek help for these conditions. A necessary precursor to help-seeking is disclosure, or the sharing of one’s diagnosis or symptoms with others. This pilot study aims to explore mental health disclosure decision-making among medical students. Design Semi-structured qualitative interviews. Setting Students were interviewed virtually using Zoom. Participants 20 students enrolled in nine American undergraduate medical education institutions (MD or DO). Method Interviews were audio-recorded and transcribed. Using Constructivist Grounded Theory (CGT) and iterative cycles of analysis with focused and theoretical coding, a preliminary framework was developed to represent mental health disclosure decision-making among medical students. Results The proposed framework presents three factors that impact students’ disclosure decisions: Assessing Anticipated Outcomes , Evaluating Priorities , and Determining Appropriate Recipients . The framework also identifies two moderating variables— Disclosure Goals and Severity and Type of Symptom s—that affect students’ perspectives on outcomes and recipients. Conclusion This pilot study highlights the complexity of student disclosure decision-making. While limited by the small sample size, the results suggest the importance of considering student perspectives on disclosure recipients, communication surrounding disclosure outcomes, and the flexibility of student schedules when pursuing future projects related to medical student well-being and mental health disclosure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".