Translating research in elder care: an introduction to a study protocol series
Bibliographic record
Abstract
BACKGROUND: The knowledge translation field is undermined by two interrelated gaps - underdevelopment of the science and limited use of research in health services and health systems decision making. The importance of context in theory development and successful translation of knowledge has been identified in past research. Additionally, examination of knowledge translation in the long-term care (LTC) sector has been seriously neglected, despite the fact that aging is increasingly identified as a priority area in health and health services research. AIMS: The aims of this study are: to build knowledge translation theory about the role of organizational context in influencing knowledge use in LTC settings and among regulated and unregulated caregivers, to pilot knowledge translation interventions, and to contribute to enhanced use of new knowledge in LTC. DESIGN: This is a multi-level and longitudinal program of research comprising two main interrelated projects and a series of pilot studies. An integrated mixed method design will be used, including sequential and simultaneous phases to enable the projects to complement and inform one another. Inferences drawn from the quantitative and qualitative analyses will be merged to create meta-inferences. OUTCOMES: Outcomes will include contributions to (knowledge translation) theory development, progress toward resolution of major conceptual issues in the field, progress toward resolution of methodological problems in the field, and advances in the design of effective knowledge translation strategies. Importantly, a better understanding of the contextual influences on knowledge use in LTC will contribute to improving outcomes for residents and providers in LTC settings.
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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 | 0.174 | 0.187 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.089 | 0.034 |
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".