Knowledge, Attitudes, and Practices (KAP) Regarding Physical Activity among Healthcare Professionals (HCPs) in Suva, Fiji
Bibliographic record
Abstract
BACKGROUND: More than 60% of the world’s population is not physically active. Physical inactivity affects all sectors of the population including among healthcare professionals (HCPs). The objective of this study was to determine the level of knowledge, attitudes and practices (KAP) regarding the concept, benefits and health consequences of physical activity (PA) among HCPs in Suva, Fiji. METHODS: This quantitative, cross-sectional study was conducted among HCPs in Suva, Fiji between 1 July 2017 and 22 September 2017. All available HCPs including doctors, nurses and paramedics who were willing to take part in the study were included. A validated self-administered questionnaire was used to assess the level of KAP regarding PA. Data was analysed with Statistical Package for the Social Sciences (SPSS) 25. RESULTS: 126 HCPs participated in this study with the majority being female (73.8%), in the age range of 33 – 42 years (47.6%), married (81.7%), from the nursing profession (54.0%) and within 0-5 years of experience (27.0%). The results showed that most of the participants (96.8%) had a high level of knowledge, positive attitudes (100%) and good practice (95%) regarding PA. CONCLUSION: Although the study participants had high levels of knowledge and positive attitudes towards PA, it is important to promote their practice. Using tailored behavioural change theories that focus on perceived benefits and barriers of PA may help decision-makers to promote PA in the workplace and among HCPs in Fiji.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".