The Back College for nurses – an evaluation of intermediate effects
Bibliographic record
Abstract
BACKGROUND: Nursing staff and care workers run an increased risk of work related musculoskeletal disorders such as low back pain. The Institution for Statutory Accident Insurance and Prevention in the Health and Welfare Services (BGW) offers its insured persons the opportunity to participate in a three-week Back College with the aim of preventing them having to abandon their profession due to back problems. The aim of the study was to record the effectiveness and sustainability of the Back College on an intermediate basis (6 months). METHODS: As part of a single-group pre-post measurement on three survey dates - at the start (T0) and end (T1) of rehabilitation and 6 months later (T2) - in 2013 all participants in the Back College at three locations were surveyed using a standard questionnaire. Wilcoxon signed-rank tests were performed to evaluate statistically significant changes. RESULTS: For measurement dates T0 to T2 we had 570 complete datasets (response rate 70.81%). There was a significant decrease in reported back pain and the general state of health and quality of life index improved. Participants' emotional strain decreased and they showed an improved understanding of illness as well as of having acquired knowledge-based abilities and skills for dealing with the disease. After training, they recorded back-friendly behaviour in everyday life and opportunities to relieve strain on the spinal column were utilised at work more often. Participants' subjective assessment of their ability to work (Work Ability Index) improved. CONCLUSION: The present study proved the intermediate effectiveness of the Back College curriculum. Whether these effects remain stable in the long term will be tested on the subsequent measurement date (T3, after 24 months).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".