THE INFLUENCE OF NURSING HOME CONTEXT ON IMPLEMENTATION: EARLY FINDINGS
Bibliographic record
Abstract
Abstract Evidence suggests that organizational context, including context of care in nursing homes (NHs) is associated with implementation and improvement success and that optimized context contributes to improved resident and staff outcomes. However, evidence on mechanisms of these influences is scarce. The longitudinal Translating Research in Elder Care program (2007 to present) is our data source. This symposium reports findings of a secondary analysis of longitudinal organizational structural and contextual, staff worklife, and resident care quality data collected in nursing homes from 2007-2021, in a cohort of 94 Canadian nursing homes. Our data include continuously collected administrative data (RAI-MDS 2.0), 5 waves of primary surveys from multiple care providers in NHs (15,000 cases to date), extensive qualitative case study data, and data from two large scale pragmatic trials. The TREC program was framed originally and continues to be, using the Promoting Action on Research Implementation in Health Services (PARiHS) framework. Each of our 5 papers will report on a critical aspect of the findings. The symposium will conclude with a discussion of implications of these findings of phase one and a description of phase two and how it is shaped by our interactions with expert panels (context and implementation experts, policymakers, NH managers, paid care providers, and residents and families). Early implications of this work for future research in nursing homes and for practice and policy will be mentioned.
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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 | 0.032 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".