Safety culture of nursing homes
Bibliographic record
Abstract
BACKGROUND: Examining the perception of patient safety culture (PSC) of top managers in health care settings is important because their orientation to PSC can have a large influence on the facility. PURPOSES: In this research, the perception of PSC of nursing home administrators (NHAs) and directors of nursing (DONs) is examined. METHODOLOGY/APPROACH: Primary data were collected to examine the opinions of NHAs and DONs regarding PSC. Information was collected from a large nationally representative sample of 4,000 nursing homes. The Nursing Home Survey on Patient Safety Culture survey instrument was used as a measure of PSC. This has 12 domains and 38 items. Bias indexes, intraclass correlation coefficients, and Pearson's product-moment correlation coefficients of the differences between NHA and DON item scores were examined. FINDINGS: Using a 0-100 scale, most scores fell into the 55-80 range. Higher scores represent a higher (more favorable) PSC. Agreement between the NHAs and DONs was excellent in 10 items, good in 15 items, moderate in 4 items, and poor in 8 items. Of the four largest differences in scores, the NHA scores were higher than the DON scores for 1 item, and DON scores were higher than the NHA scores for 3 items. IMPLICATIONS: The overall perception from both NHAs and DONs appear to represent a somewhat "positive" outlook from these top managers on their institution's PSC. However, NHAs in general report higher scores than DONs do. The areas of divergence between these top managers are further discussed, with a view toward directing future patient safety investigations and initiatives in nursing homes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".