Knowledge translation case study: A rural community collaborates with researchers to investigate health issues
Bibliographic record
Abstract
INTRODUCTION: Knowledge translation implies the exchange and synthesis of knowledge between researchers and research users, employing a high level of communication and participation, not only to share the knowledge found through research, but also to implement subsequent strategies. Prince Edward Island, a rural province in Canada, provided the setting to exchange knowledge between researchers and a rural community on the health issues affecting children. METHODS: A case study reports census data, demographic trends, and information about health issues immediate to the community. These focus groups were held to plan solutions to the community's health priorities. The process was participatory, characterized by community involvement. RESULTS: Those participating in the focus groups were interested in research findings and literature to solve local problems. Parenting and mental health were determined to be priority issues requiring broader community engagement. The process of translating knowledge into action after the focus groups met lacked widespread involvement of the community. DISCUSSION: Although encouraged to do so, the larger rural community did not participate in examining research findings or in planning interventions. The parents in this community may not have perceived themselves as having influence in the process or goals of the project.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".