Engaging the Community: Knowledge Translation as Transformation in the Lives of Children in One Rural Community of Prince Edward Island
Bibliographic record
Abstract
This research involves transdisciplinary, participatory research to identify strategies, approaches, tools, and resources that promote effective knowledge translation related to health in the rural communities of Prince Edward Island (PEI). Partnerships established with six rural PEI communities enabled researchers to identify and evaluate effective knowledge translation strategies. Interactive engagement of communities results in the most effective knowledge translation (St Croix 2001) and forms the basis of this research project. Knowledge translation has become a priority for many research organizations (Canadian Institute of Population Health 2002) because many decision makers have not used academic research findings in developing programs or policies (Barahamson 1996 & Mowday 1997). This gap is found in nearly all fields in which there are both practitioners and researchers. Preliminary findings from focus group interviews involving parents, youth and service providers from rural communities on PEI are presented as a means of addressing this gap. This research demonstrates that when rural communities are engaged in unique, participatory forms of relationship building and approaches that translate research results into meaningful information and programs, positive changes in a community’s attitudes and behaviours will result. This article describes how members of one rural community engaged with researchers and used knowledge gained from the results of research to established a youth centre for their children.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".