Is non-medical use of prescription sedatives and sleeping pills associated with symptoms of depression, anxiety, and stress in undergraduate university students
Bibliographic record
Abstract
Introduction: Mental health is a public health concern on university campuses. However, little is understood about the etiology of mental health in this population. Purposes: To measure the association between the non-medical use of sedative and sleeping pills in the past three months and moderate-extremely severe symptoms of depression, anxiety, and stress in the past week, in undergraduate students at the University of Ontario Institutes of Technology (University of Ontario Institute of Technology). Methods: Cross-sectional study of undergraduate students enrolled in the faculty of health sciences and faculty of education at University of Ontario Institute of Technology in the Fall semester of 2017. Findings: Few students reported lifetime (7.8%) and past three month (3.7%) non-medical sedative and sleeping pill use. More students reported moderate-extremely severe symptoms of depression (30.3%), anxiety (47.3%), and stress (25.5%). I found no association between non-medical sedative and sleeping pill use and moderate-extremely severe symptoms of anxiety, stress, and depression. Discussion: Despite no association between non-medical sedative and sleeping pills use and symptoms of anxiety, stress, and depression, students must be educated about the potential negative health impacts of non-medical sedative and sleeping pill use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".