OP48 Nursing Requirements In Long-Term Care: A Health Technology Assessment
Bibliographic record
Abstract
Introduction The objectives of this study were to systematically review published research on the relationship between nursing staff coverage, care hours, and quality of care (QoC) in long-term care (LTC) facilities; and to conduct a real world evidence (RWE) analysis using Alberta real world data (RWD) to inform policy makers on whether any amendments could be made to current regulations. Methods A systematic review (SR) of research evidence published between January 2000 and May 2018 on the relationship between nursing staff coverage, care hours, and QoC in LTC facilities was conducted. Panel data regressions using available RWD from Alberta, Canada, were performed to assess associations between nursing care hours and LTC outcomes. Outcomes of interest included quality indicators related to resident outcomes, hospital admissions, emergency room visits and family satisfaction. Nursing care hours considered in SR and RWE analysis included those provided by registered nurses (RNs) and licensed practical nurses (LPNs). Results The SR found inconsistent and poor quality evidence relevant to the questions of interest, indicating a great uncertainty about the association between nursing staff time and type of coverage and QoC. Although some positive indications were suggested, major weaknesses of reviewed studies limited interpretation of SR results. RWE analysis found that impact of care hours on LTC outcomes was heterogeneous, dependent on outcome measurements. There was evidence that total staff, RN, and LPN hours had positive effects on some resident outcomes and magnitude of effect differed for different nursing staff. Conclusions No definitive conclusion could be drawn on whether changing nursing staff time or nursing staff coverage models would affect residents’ outcomes based on the research evidence gathered in the SR. RWE analysis helped to fill a gap in the available published literature and allowed policy makers to better understand the impact of revising current regulations based on actual outcomes.
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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 | 0.112 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.019 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".