Knowledge, Attitude and Practices (KAP) towards Physical Activity (PA) among Medical Academic Staff in Fiji: A Mixed Method Study
Bibliographic record
Abstract
BACKGROUND: Regular PA is one of the most important practices that a person can do to stay healthy. Physical inactivity has been identified as the fourth leading risk factor for global mortality. Workforces are facing health issues such as non-communicable diseases (NCDs) due to sedentary lifestyle. OBJECTIVE: To identify the Knowledge, Attitude and Practice (KAP) towards PA amongst medical academic staff at Fiji National University (FNU). METHODS: This is a mixed methods study design conducted amongst all academic and senior management staff at the 5 schools at College of Medicine, Nursing and Health Sciences (CMNHS) at FNU in 2019. A self-administered questionnaire was used to collect quantitative data which was authenticated and face and content validated in a pilot study. A semi-structured questionnaire was used to guide Focus Group Discussion (FGD) amongst senior managers at CMNHS. Descriptive analysis was conductedfor quantitative data while thematic analysis was done to analyze the qualitative data. RESULTS: The knowledge on PA was seen to be at a medium level 19.2 (±2.8), while attitude was found to be of high level 32.62 (±2.86) followed by practice seen as a good practice 11.93 (±1.39) amongst academic staffs of CMNHS. Four major themes were identified from the qualitative including; types of PA available in schools, university contribution towards PA, barriers of PA in CMNHS and recommendations for PA. CONCLUSION: CMNHS needs to strengthen the wellness approaches that increase opportunities towards promoting or engaging into PA related work. Further research is warranted to determine on effective ways to increase PA among academic staff atCMNHS.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".