Reasons for why Medical Students Prefer Specific Sleep Management Strategies
Bibliographic record
Abstract
OBJECTIVES: Insomnia symptoms are common among medical students. This study explored the perspectives of medical students about which sleep management strategies to use. METHODS: Medical students responded to an online survey on their thoughts about the use of various sleep management strategies. RESULTS: Of the 828 respondents, 568 (69%) provided responses to questions about the most preferred strategies and 450 (54%) provided responses about their least preferred strategies. About 48.5% felt their insomnia symptoms were too mild to see a clinician and 23.9% did not think their symptoms warranted sleep medication. Over 40% of students could not avoid work before sleep, have consistent sleep/wake times, or engage in regular exercise because of their busy and inconsistent schedules. Approximately 40-60% could not improve their sleep environment (e.g. better heating and bed) because of the associated costs. Over 80% reported an inability to change their pre-sleep habits (e.g. using electronics close to bedtime, using bed for activities other than sleep or sex). Half of the students disliked relaxation techniques or felt they would not help. Around 30-50% did not believe that changing caffeine and/or alcohol intake would affect their sleep. CONCLUSIONS: Medical students may benefit from additional sleep education. Clinicians may need to discuss which strategies individual students prefer and modify their recommendations accordingly.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".