Factors Influencing University Staff Health-Promoting Lifestyle Behaviours in Nigeria: A Qualitative Descriptive Study
Bibliographic record
Abstract
Introduction: The role of health promoting-lifestyle in the prevention of noncommunicable diseases that is global epidemics cannot be emphasis. This study examined resources available in a university that enhance and maintain health-promoting lifestyle behaviour of staff and explore factors influencing health-promoting lifestyle behaviour of staff. Methods: The study adopted a qualitative descriptive study design. The study setting was a university with multiple satellite campuses and a staff total of 2,657 at the time of data collection. Data were collected from both academic and non-academic staff of the university through in-depth interviews. Data were analysed using content analysis and Nvivo version 11 was used for data management. Results: Health promotion resources available in the institution were a health facility, a nutritional facility and a physical and fitness facility. The findings revealed that factors influencing health-promoting lifestyle behaviour of staff were lack of institutional health policy and protocol, work overload, lack of planned and consistent health promotion awareness, and economic factors. The majority of our participants did not see health facilities as a means of health promotion; instead they saw it as a resource to be used when they were sick rather than for health promotion services like health screening. Conclusion: The study concluded that institutional health policy and protocol is key in improving the health of workers. Healthy workers made a healthy institution and for institutional aim to be achieved, workers need to be healthy. Therefore, emphasis should be placed on preventive management than curative management.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".