Stakeholder Perspectives of Attributes and Features of Context Relevant to Knowledge Translation in Health Settings: A Multi-Country Analysis
Bibliographic record
Abstract
BACKGROUND: Context is recognized as important to successful knowledge translation (KT) in health settings. What is meant by context, however, is poorly understood. The purpose of the current study was to elicit tacit knowledge about what is perceived to constitute context by conducting interviews with a variety of health system stakeholders internationally so as to compile a comprehensive list of contextual attributes and their features relevant to KT in healthcare. METHODS: A descriptive qualitative study design was used. Semi-structured interviews were conducted with health system stakeholders (change agents/KT specialists and KT researchers) in four countries: Australia, Canada, the United Kingdom, and the United States. Interview transcripts were analyzed using inductive thematic content analysis in four steps: (1) selection of utterances describing context, (2) coding of features of context, (3) categorizing of features into attributes of context, (4) comparison of attributes and features by: country, KT experience, and role. RESULTS: A total of 39 interviews were conducted. We identified 66 unique features of context, categorized into 16 attributes. One attribute, Facility Characteristics, was not represented in previously published KT frameworks. We found instances of all 16 attributes in the interviews irrespective of country, level of experience with KT, and primary role (change agent/KT specialist vs. KT researcher), revealing robustness and transferability of the attributes identified. We also identified 30 new context features (across 13 of the 16 attributes). CONCLUSION: The findings from this study represent an important advancement in the KT field; we provide much needed conceptual clarity in context, which is essential to the development of common assessment tools to measure context to determine which context attributes and features are more or less important in different contexts for improving KT success.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".