Pedometer-determined physical activity profile of healthcare professionals in a Nigerian tertiary hospital
Bibliographic record
Abstract
BACKGROUND: Healthcare professionals (HCPs) are perceived as statutory advocates for healthy living and promotion of healthy behaviors such as regular participation in physical activity (PA). This study assessed and compared pedometer-determined PA of different urban HCPs in a Nigerian tertiary hospital. MATERIALS AND METHODS: A cross-sectional study involving 180 HCPs from a tertiary hospital in Lagos, Nigeria. PA was measured by daily walking steps using a pedometer. RESULTS: The mean step count obtained was 7,396.94 ± 2,714.63 steps/day. Only 20% of the HCPs met a minimum PA of 10,000 steps/day. About one-third (34.4%) of the HCPs were low active and less than a quarter (23.9%) were somewhat active. Further, less than half (43.9%) of the HCPs were found to have PA levels ≥7,500 steps/day. Overall, nurses had the highest step counts (7,980 steps/day) followed by physiotherapists (7,332 steps/day), while pharmacists had the lowest step counts (6,201 steps/day). There was however no significant difference in the mean step counts of the various cadres of the HCPs (P > 0.05). Step counts of HCPs were found to significantly negatively correlate with their age (r = -0.53; P < 0.001), body mass index (r = -0.39; P < 0.001), and body fat percentage (r = -0.42; P < 0.001). CONCLUSION: PA profile of the HCPs was mostly characterized by a low active PA level and less than a quarter met the recommended minimum of 10,000 steps/day.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".