Un outil pour accompagner la transférabilité des interventions en promotion de la santé : ASTAIRE
Bibliographic record
Abstract
The complexity of health promotion interventions raises the problem of the transferability of their results from one setting to another. A tool has been developed and validated: ASTAIRE (AnalySe de la Transférabilité et Accompagnement à l'adaptation des InteRventions en promotion de la santE) (analysis of the transferability and support to adaptation of health promotion interventions). The purpose of this article is to present the French language version of this tool to enable French-speaking stakeholders and scientists to adopt this tool and use it for the purposes of development of evidence-based health promotion. ASTAIRE comprises 23 transferability criteria classified in four categories: population, environment, implementation, transfer support. It is composed of two grids, one for reporting of initial interventions according to transferability criteria and the other to analyse the comparability of settings and to facilitate transfer. This tool is designed to support the choice of the intervention most adapted to the setting and to facilitate transfer of this intervention. Use of this tool can promote the development of evidence-based approaches according to an adaptive logic of interventions. Collective use of this tool in project logics can distinguish the key functions of interventions, which determine their efficacy and which must be transferred, from aspects related to the form, which can be adapted to the setting.
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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 | 0.171 | 0.283 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".