Nursing assistants' use of best practices and pain in older adults living in nursing homes
Bibliographic record
Abstract
BACKGROUND: Inadequate pain management persists in nursing homes. Nursing assistants provide the most direct care in nursing homes, and significantly improving the quality of care requires their adoption of best care practices informed by the best available evidence. We assessed the association between nursing assistants' use of best practices and residents' pain levels. METHODS: We performed a cross-sectional analysis of data collected between September 2019 and February 2020 from a stratified random sample of 87 urban nursing homes in western Canada. We linked administrative data (the Resident Assessment Instrument-Minimum Data Set [RAI-MDS], 2.0) for 10,093 residents and survey data for 3547 nursing assistants (response rate: 74.2%) at the care unit level. Outcome of interest was residents' pain level, measured by the pain scale derived from RAI-MDS, 2.0. The exposure variable was nursing assistants' use of best practices, measured with validated self-report scales and aggregated to the unit level. Two-level random-intercept multinomial logistic regression accounted for the clustering effect of residents within care units. Covariates included resident demographics and clinical characteristics and characteristics of nursing assistants, unit, and nursing home. RESULTS: Of the residents, 3305 (30.3%) were identified as having pain. On resident care units with higher levels of best practice use among nursing assistants, residents had 32% higher odds of having mild pain (odds ratio, 1.32; 95% confidence interval, 1.01-1.71; p = 0.040), compared with residents on care units with lower levels of best practice use among nursing assistants. The care units did not differ in reported moderate or severe pain among residents. CONCLUSIONS: We observed that higher unit-level best practice use among nursing assistants was associated with mild resident pain. This association warrants further research to identify key individual and organizational factors that promote effective pain assessment and management.
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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 | 0.002 | 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 | 0.000 |
| 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".