Creating Bridges Between Researchers and Long-Term Care Homes to Promote Quality of Life for Residents
Bibliographic record
Abstract
Improving the quality of life for long-term care (LTC) residents is of vital importance. Researchers need to involve LTC staff in planning and implementing interventions to maximize the likelihood of success. The purposes of this study were to (a) identify barriers and facilitators of LTC homes' readiness to implement evidence-based interventions, and (b) develop strategies to facilitate their implementation. A mixed methods design was used, primarily driven by the qualitative method and supplemented by two smaller, embedded quantitative components. Data were collected from health care providers and administrators using 13 focus groups, 26 interviews, and two surveys. Findings revealed that participants appreciated being involved at early stages of the project, but receptiveness to implementing innovations was influenced by study characteristics and demands within their respective practice environment. Engaging staff at the planning stage facilitated effective communication and helped strategize implementation within the constraints of the system.
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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 | 0.200 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".