Prevalence of Depression and Associated Factors among Secondary School Teachers in Medina Region, Saudi Arabia
Bibliographic record
Abstract
Background: Depression is one of the most common psychiatric conditions seen in primary health care clinics. On the other hand, teaching is one of the most stressful jobs. Objective: The aim was to estimate the prevalence and risk factors of depression among secondary school teachers in the Medina region. Subjects and Methods: A cross-sectional descriptive study was conducted on a representative sample of secondary school teachers of both genders in the Medina region. A multistage cluster sample technique was implemented to recruit the participants. The data were collected using a self-administered Patient Health Questionnaire-9 (PHQ-9) depression questionnaire. Results: The study included 297 teachers. More than half of them (54.2%) were males and the vast majority (97.6%) were Saudis. Major depression, based on PHQ-9 depression questionnaire was reported among 8.4% of the participants, whereas minor depression was reported among 14.1% of them. Among the studied habitual factors, the smoking of Shisha and nonpracticing of physical exercise were significantly associated with depression among teachers. Regarding obstetric and gynecological factors, female teachers with a history of postnatal depression were more likely to have major and minor depression features than those without such history. Conclusion: Depression is a common health problem among secondary school teachers in Medina region affecting almost one quarter of them. Familial instability, shisha smoking, physical inactivity, heavy teaching load, and deficiency of educational competencies are strongly correlated with depression. Further studies are needed to explore the extent of the problem among teachers at different levels of teaching across the kingdom.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".