Patient transfer skills and safety culture
Bibliographic record
Abstract
Background: Nursing practice includes a lot of patient handling and transfer movement, with high risk of work related back injuries. The article discusses employee perspectives on the meaning of a multi-component intervention and its impact on ergonomic patient transfer practice and safety culture. Method: This was a qualitative study using content analysis approach. Data were answers to open questions about patient transfer practice and the meaning of a multi-component intervention carried out in one Norwegian municipality. Research focus were on patient transfer skills, safety culture, and psychosocial climate at the workplace. Data gathered one and a half year after termination of the intervention. Purposive sampling included sixty-one health care personnel. All had been participating in the intervention. Results: The analysis revealed the theme “Competence, practice and health impact” with sub themes “Measures facilitates change” and “Influence over time”. The intervention seemed to promote a safety climate with positive impact on employees’ health. Further, the transfer movements were more comfortable and safe for the patients and they became more self-reliant. Comprehensive, educational, and technical measures facilitated for change. After intervention termination, the intervention had persistent influence over time on daily ergonomic patient transfer practices. Findings also revealed some challenges. Conclusion: The findings shed light on impact of management that focus on comprehensive educational measures for an entire staff at a local work place. The study do not provide transferability to other contexts, but nurse leaders can use study findings to inform their efforts on learning and culture change among the workforce.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".