Occupational differences in workers' compensation indemnity claims among direct care workers in Minnesota nursing homes, 2005‐2016
Bibliographic record
Abstract
BACKGROUND: Nursing assistants have one of the highest injury rates in the U.S., but few population-based studies assess differential injury risk by occupation in nursing homes. This statewide study assessed differences in musculoskeletal disorders (MSDs) and patient handling injuries among direct care workers in Minnesota nursing homes. METHODS: Indemnity claims from the Minnesota workers' compensation database were matched to time at risk from the Minnesota Nursing Home Report Card to estimate 2005 to 2016 injury and illness claim rates for certified nursing assistants (CNAs), licensed practical nurses (LPNs), and registered nurses (RNs). Associations between occupation and claim characteristics were assessed using multivariable regression modeling. RESULTS: Indemnity claim rates were 3.68, 1.38, and 0.69 per 100 full-time equivalent workers for CNAs, LPNs, and RNs, respectively. Patient handling injuries comprised 62% of claims. Compared to RNs, CNAs had higher odds of an indemnity claim resulting from an MSD (odds ratio [OR] = 1.67; 95% confidence interval [CI], 1.31-2.14) or patient handling injury (OR = 1.89; 95% CI, 1.47-2.45) as opposed to another type of injury or illness. CNAs had lower odds of receiving temporary and permanent partial disability benefits and higher odds of receiving a stipulation settlement. CONCLUSIONS: CNAs in Minnesota nursing homes are at heightened risk for lost time MSDs and patient handling injuries. Claims filed by CNAs are more frequently settled outside the regular workers' compensation benefit structure, an indication that the workers' compensation system is not providing adequate and timely benefits to these workers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".