Assessing Resident Safety Culture in Nursing Homes
Bibliographic record
Abstract
OBJECTIVES: To examine the overall responses of nursing home staff to a newly developed nursing home specific survey instrument to assess patient safety culture (PSC) and to examine whether nursing home staff (including administrator/manager, licensed nurse, nurse aide, direct care staff, and support staff) differ in their PSC ratings. METHODS: Data were collected in late 2007 through early 2008 using a survey administered to staff in each of 40 nursing homes. In 4 of these nursing homes, the responses of different staff were identified. The Nursing Home Survey on Patient Safety Culture was used to assess the 12 domains of the PSC and identify differences in PSC perceptions between staff. RESULTS: For the 40 nursing homes in the sample, the overall facility response rate was 72%. For the 4 nursing homes of interest, the overall facility response rate was 68.9%. The aggregate Nursing Home Survey on Patient Safety Culture scores, using all staff types for all survey items, show that most respondents report a poor PSC. However, administrators/managers had more positive scores than the other staff types (P < 0.05) across most domains. CONCLUSIONS: Staff in nursing homes generally agree that PSC is poor. This may have a significant impact on quality of care and quality of life for residents.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 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".