Using Concept Maps to compare obesity knowledge between policy makers and primary care researchers in Canada
Bibliographic record
Abstract
OBJECTIVE: Knowledge transfer is the process of information sharing between researchers, knowledge users and policy makers. Globally, public policies about obesity do not reflect the complexity of what is known about the cause and effects of obesity. We used Concept Maps, a qualitative method that represents mental models, to compare the understanding of obesity between policy makers in a Canadian province and local primary care researchers. Eight participants were interviewed during which a Concept Map was developed using "C-map Tools" software. Maps were then colour-coded to identify themes and concepts in the maps. Finally, the team synthesised the findings from each of the maps and presented them back to each of the participants. RESULTS: All participants had mental models with rich details on the complexity of obesity for individuals, community, and at the policy level. Clinician-researchers had more focus on medical management than policy makers although most participants lacked concepts on the role of primary care in obesity management. A shared understanding of obesity could assist researchers and policy makers in developing a relevant and effective strategy. Concept Mapping provides a novel and creative way to visually compare different understandings of health-related topics.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".