Sleep Disturbances Rate among Medical and Allied Health Professions Students in Iran: Implications from a Systematic Review and Meta-Analysis of the Literature
Bibliographic record
Abstract
Medicine and healthcare professions are prestigious and valued careers and, at the same time, demanding, challenging, and arduous jobs. Medical and allied health professions students, experiencing a stressful academic and clinical workload, may suffer from sleep disturbances. In Iran, several studies have been conducted to explore the prevalence rate among medical and healthcare professions students. The aim of this systematic review and meta-analysis was to quantitatively and rigorously summarize the existing scholarly literature, providing the decision- and policy-makers and educators with an updated, evidence-based synthesis. Only studies utilizing a reliable psychometric instrument, such as the Pittsburgh sleep quality index (PSQI), were included, in order to have comparable measurements and estimates. Seventeen investigations were retained in the present systematic review and meta-analysis, totaling a sample of 3586 students. Studies were conducted between 2008 and 2018 and reported an overall rate of sleep disturbances of 58% (95% confidence interval or CI 45-70). No evidence of publication bias could be found, but formal analyses on determinants of sleep disturbances could not be run due to the dearth of information that could be extracted from studies. Poor sleep is highly prevalent among Iranian medical and healthcare professions students. Based on the limitations of the present study, high-quality investigations are urgently needed to better capture the determinants of poor sleep quality among medical and healthcare professions students, given the importance and the implications of such a topic.
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.027 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".