A tool to analyze the transferability of health promotion interventions
Bibliographic record
Abstract
BACKGROUND: Health promotion interventions are often complex and not easily transferable from one setting to another. The objective of this article is to present the development of a tool to analyze the transferability of these interventions and to support their development and adaptation to new settings. METHODS: The concept mapping (CM) method was used. CM is helpful for generating a list of ideas associated with a concept and grouping them statistically. Researchers and stakeholders in the health promotion field were mobilized to participate in CM and generated a first list of transferability criteria. Duplicates were eliminated, and the shortened list was returned to the experts, scored for relevance and grouped into categories. Concept maps were created, then the project team selected the definitive map. From the final list of criteria thus structured, a tool to analyze transferability was created. This tool was subsequently tested by 15 project leaders and nine experts. RESULTS: In all, 18 experts participated in CM. After testing, a tool, named ASTAIRE, contained 23 criteria structured into four categories: population, environment, implementation, and support for transfer. It consists of two tools--one for reporting data from primary interventions and one for analyzing interventions' transferability and supporting their adaptation to new settings. CONCLUSION: The tool is helpful for selecting the intervention to transfer into the setting being considered and for supporting its adaptation. It also facilitates new interventions to be produced with more explicit transferability criteria.
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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 | 0.114 | 0.359 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.044 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".