Examining Linkages between Psychological Health Problems, Socio-Demographic Characteristics and Workplace Stressors in Pakistan’s Academia
Bibliographic record
Abstract
Scholarly work and research are globally known as stressful and challenging. Teachers may develop different psychological health problems once they are exposed to workplace stressors. Considering it as a serious issue of education sector, this study has examined the linkages between prevalent workplace stressors and psychological health problems in Pakistan’s academia. A cross-section quantitative research design was adopted, whereas data was collected by self-administered questionnaire from a sample of 1189 teachers working within 12 universities of Pakistan. Descriptive statistics were used for analysis of demographic data; Chi-square tests were performed to compare psychological health problems with socio-demographic characteristics, whereas Multivariate Logistic Regression Analysis was run to know the relationship between prevalent work stressors and psychological health problems. Results show that the majority of respondents were male (63.8%), unmarried (58.5%), 25 to 40 years old (78.5%) and working as Lecturers or Assistant Professors (77%), thus their average job experience was 05 to 10 years. Psychological health problems were prevailing more among male, unmarried, less experienced and junior teachers. Such socio-demographic characteristics were potential risk factors for psychological health problems. Furthermore, respondents exposed to work stressors like workload, interpersonal and emotional demands were more likely to develop psychological health problems. Findings of the current study will develop awareness among teachers concerning work stress and psychological health. Such findings can be utilized by policy makers of Pakistan for devising policies about occupational health & safety of teachers. The prevalence of psychological health problems in academia of Pakistan is a recognized workplace issue. Therefore, it needs immediate corrective measures at individual and institutional levels.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".