How Context Shapes the Experience of Staff and Residents in Residential Long-Term Care Settings
Bibliographic record
Abstract
Abstract This chapter describes 10 years of research into organizational context in residential long-term care (LTC) settings. It focuses on this book’s first and third questions: What constitutes context for an event, situation, or phenomenon? And how do contexts change, and what is the role of actors in such processes? Although with respect to change, it does not focus as much on secular trends as it does on strategies to improve local context. We explore how context influences use of research by staff, quality-of-life indicators for staff, and ability to improve quality of care and quality of life for LTC residents. First, it describes the development and ongoing use of the Alberta Context Tool. Second, it describes the Translating Research in Elder Care (TREC) program of research, and the LTC setting in which the authors study context to bring about quality improvements. Third, it presents selected empirical findings as evidence that context matters in LTC. Finally, it proposes future directions to understand and modify context for improved quality in LTC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".