Ergonomic principles in patient handling: Knowledge and practice of physiotherapists in Nigeria
Bibliographic record
Abstract
BACKGROUND: Physiotherapists are advocates of workplace health and safety. Despite the high prevalence of work-related musculoskeletal disorders (WMSDs), there is limited knowledge of ergonomic principles have been successfully applied in the workplace by Nigerian physiotherapists. OBJECTIVES: This study evaluates the knowledge and practice of ergonomic principles in patient handling among physiotherapists in Nigeria. METHOD: A cross-sectional survey design was used to sample 360 physiotherapists practicing in Nigeria. Participants responded to a three-part structured questionnaire that had a reliability coefficient of 0.77. Data was analyzed using descriptive statistics and Chi-Square. RESULTS: The majority (95.9%) of the participants had good knowledge of the ergonomic principles in patient handling while only 48.6% reported practicing them. Poor practice was mainly due to a lack of patient handling equipment. There was no significant association between knowledge and practice of ergonomic principles among study participants. Specific areas of physiotherapy practice showed a significant association with ergonomic knowledge and practice. Years of physiotherapy practice and highest educational qualifications showed a significant association with the levels of practice and knowledge respectively. CONCLUSION: Physiotherapists in Nigeria reported a good level of knowledge of ergonomic principles, but a poor practice level. Perhaps this non-adherence contributed to the high prevalence of WMSDs among physiotherapists in Nigeria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".