Occupational Stress and Academic Staff Job Performance in Two Nigerian Universities
Bibliographic record
Abstract
Available reports provide an account of academic staff’s poor job performance in higher education institutions and universities in particular. Consequently, a growing body of research has been attracted to this area, including those seeking ways to understand the problem and others aimed at proffering solutions. This study contributes to the literature by investigating the influence of occupational stress on the job performance of academic staff in universities. Three null hypotheses directed the study in line with the quantitative ex-post facto research design. A sample of 150 respondents was obtained using the systematic random sampling technique from a population of 400 lecturers in the Faculty of Education from two public universities in Nigeria. A 31-item questionnaire was used for data collection. The null hypotheses were tested at the .05 alpha level using simple linear regression analysis. It was revealed that remuneration is a significant positive predictor of academic staff job performance. The prediction of workload was negatively non-significant on the job performance of academics. The provision of institutional amenities has a positive but non-significant prediction on academic staff job performance in the two public universities. It was concluded that occupational stress significantly influences the job performance of lecturers in universities. The study recommended that the government constantly pay lecturers’ salaries as and when due. Institutional managers should reward lecturers with outstanding performance to boost their morale for effective service delivery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".