Context-informed population health knowledge translation : a case study
Bibliographic record
Abstract
Background: Population health evidence suggests a broad range of contexts for the use of knowledge. Researchers identify the need for new services, and they suggest ways to prevent exposure to known harm, often through public policy or structural changes. These suggestions are often beyond the funding and mandates of existing health service organizations. There is still limited guidance to inform knowledge translation when implementation requires collaboration between multiple institutions or where the use of knowledge calls for new resources or infrastructure. Method: This research investigates context-informed knowledge translation to improve population health. It collects and analyzes interview data from experienced knowledge translation practitioners in a case study organization that seeks to promote universal programs and public policy to improve health at a population level. Knowledge translation is examined at two levels, first at an organizational level and then in two sub cases that represent divergent knowledge products and different contextual barriers and opportunities. Qualitative analysis is used to investigate the range of approaches used and the rationale for the use of specific approaches in different contexts. Discussion: Findings suggest that there are identifiable links between the nature of contextual challenges and the approaches used in the case study. Challenges for knowledge translation can be conceptually divided into three categories: 1. Ensuring reach and understanding, 2. Ensuring capacity for implementation, and 3. Ensuring that those positioned to act effectively on the knowledge are motivated to do so. Findings suggested that the categories of approach used by knowledge translation practitioners correspond with the nature of identified challenges: exchange and transfer of information to build awareness and understanding, processed focused approaches to build implementation capacity, and strategic approaches to persuade or motivate uptake. Findings operationalize knowledge, context, and facilitation in ways that can be used in further study of conditions where knowledge translation may need to build capacity or motivation to advance the use of reliable knowledge. Practitioners can use the proposed categories to identify context specific challenges and can then draw from the hierarchically structured menu of approaches to build theories of change that can plausibly address them.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".