To whom health care aides report: Effect on nursing home resident outcomes
Bibliographic record
Abstract
INTRODUCTION: Health care aides (personal support workers and nursing assistants) provide ~80%-90% of direct care to residents in nursing homes; it is therefore important to understand whether supervision of health care aides affects quality of care. We sought to determine whether health care aide reporting practices are associated with resident outcomes in nursing homes. DESIGN AND METHODS: We conducted a cross-sectional secondary analysis of survey data of 3991 health care aides from 322 units in 89 nursing homes in Western Canada. We then linked resident data from the Resident Assessment Instrument-Minimum Data Set (RAI-MDS) 2.0 database to care aide surveys at the unit level. We used hierarchical mixed models to determine if the proportion of health care aides reporting to a respective nursing leader role was associated with 13 practice sensitive quality indicators of resident care. RESULTS: Most health care aides reported to a registered nurse (RN, 44.5%) or licenced practical nurse (LPN, 53.3%). Only 2.2% of health care aides reported to a care manager and were excluded from the analysis. Resident outcomes for only declining behavioural symptoms were more favourable when a higher proportion of health care aides (on a unit) reported to RNs, β = -0.004 (95% CI -0.006, -0.001, p = .004). The remaining indicators were not affected by care aide reporting practices. DISCUSSION AND IMPLICATIONS: Resident outcomes as evaluated by the indicators appear largely unaffected by care aide reporting practices. LPNs' increasing scope of practice and changing work roles and responsibilities in nursing homes across Western Canada may explain the findings.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".