The Influence of Socio-demographic Variables on Coping Strategies for Stress and Depression among Lecturers in Selected Universities of Ogun State, Nigeria
Bibliographic record
Abstract
Aim: This research investigated the relationship between socio-demographic factors and coping mechanisms for stress and depression among lecturers in selected universities in Ogun State, Nigeria. Sample: A sample population of 285 lecturers from three universities participated in the study. Place and Duration: The study was conducted at three purposively selected universities in Ogun State, Nigeria. Methodology: A total of 285 lecturers participated in the study, representing a response rate of 92%. Data were collected using questionnaires distributed physically and online. Socio-demographic variables such as age, gender, educational level, type of university, academic rank, and years of service were assessed, alongside coping mechanisms for stress and depression. Results: Analysis revealed a diverse range of socio-demographic characteristics within the sample. The study found a moderate utilization of adaptive coping mechanisms for stress and depression, while the prevalence of maladaptive coping strategies remained low. Significant correlations emerged between socio-demographic variables such as age, gender, education level, and academic rank, and both adaptive and maladaptive coping strategies for stress. Additionally, age and gender demonstrated significant associations with coping strategies for depression. Conclusion: These findings underscored the pivotal role of socio-demographic factors in shaping the frequency and nature of coping mechanisms adopted by lecturers. Implications for the design of targeted support interventions within academic environments were discussed. Recommendations: Universities should prioritize implementing policies and training programs that promote adaptive coping mechanisms for stress and depression among lecturers, fostering a supportive environment that enhances their well-being, resilience, and professional effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".