Differential effectiveness of the Minnesota Safe Patient Handling Act by health care setting: An exploratory study
Bibliographic record
Abstract
BACKGROUND: The Minnesota Safe Patient Handling (MN SPH) Act requires health care facilities to implement comprehensive programs to protect their workers from musculoskeletal injuries caused by lifting and transferring patients. Nursing homes, hospitals, and outpatient facilities each face unique challenges implementing and maintaining SPH programs. The objective of the study was to compare patient handling injuries in these three health care settings and determine whether change in injury rate over time differed by setting following enactment of the law. METHODS: Workers' compensation data from a Minnesota-based insurer were used to describe worker and claim characteristics in nursing homes, hospitals, and outpatient facilities. Negative binomial models were used to compare claims and estimate mean annual patient handling claim rates by health care setting and time period following enactment of the law. RESULTS: Consistent with national data, the patient handling claim rate was highest in Minnesota nursing homes (168 claims/$100 million payroll [95% confidence interval: 163-174]) followed by hospitals (35/$100 million payroll [34-37]) and outpatient facilities (2/$100 million payroll [1.8-2.2]). Patient handling claims declined by 38% over 10 years following enactment of the law (vs. 27% for all other claims). The change in claims over time did not differ by health care setting. CONCLUSIONS: In this single-insurer sample, declines in workers' compensation claims for patient handling injuries were consistent across health care settings following enactment of a state SPH law. Though nursing homes experienced elevated claim rates overall, results suggest they are not lagging hospitals and outpatient facilities in reducing patient handling injuries.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".