Understanding context in knowledge translation: a concept analysis study protocol
Bibliographic record
Abstract
AIM: To conduct a concept analysis of clinical practice contexts (work environments) that facilitate or militate against the uptake of research evidence by healthcare professionals in clinical practice. This will involve developing a clear definition of context by describing its features, domains and defining characteristics. BACKGROUND: The context where clinical care is delivered influences that care. While research shows that context is important to knowledge translation (implementation), we lack conceptual clarity on what is context, which contextual factors probably modify the effect of knowledge translation interventions (and hence should be considered when designing interventions) and which contextual factors themselves could be targeted as part of a knowledge translation intervention (context modification). DESIGN: Concept analysis. METHODS: The Walker and Avant concept analysis method, comprised of eight systematic steps, will be used: (1) concept selection; (2) determination of aims; (3) identification of uses of context; (4) determination of defining attributes of context; (5) identification/construction of a model case of context; (6) identification/construction of additional cases of context; (7) identification/construction of antecedents and consequences of context; and (8) definition of empirical referents of context. This study is funded by the Canadian Institutes of Health Research (January 2014). DISCUSSION: This study will result in a much needed framework of context for knowledge translation, which identifies specific elements that, if assessed and used to tailor knowledge translation activities, will result in increased research use by nurses and other healthcare professionals in clinical practice, ultimately leading to better patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.128 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".