An Introduction to Concept Mapping as a Participatory Public Health Research Method
Bibliographic record
Abstract
In this article, the authors introduce concept mapping as a useful participatory research method for public health researchers interested in generating hypotheses and developing theory. The authors first provide an overview of concept mapping, which combines qualitative approaches with quantitative analytical tools to produce visual displays of the relationship between ideas. Then, they present an illustrative research application of the method to the exploration of women's perceptions of the relationship between residential neighborhood factors and intimate partner violence experiences. They give attention to the data collection and analysis procedures and to demonstrating the intricacies of using concept mapping for public health research purposes. Finally, the article concludes with a discussion of the unique contributions and challenges associated with concept mapping.
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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 | 0.056 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".