L’Outil diagnostique de l’action en partenariat : fondements, élaboration et validation
Bibliographic record
Abstract
Objectives: Intervening on social determinants of health requires that public health stakeholders enter into intersectoral partnerships. The lack of valid tools to evaluate the quality of partnerships is a significant constraint to formulating convincing arguments for this kind of action. In light of this shortcoming, the tool described in this article evaluates processes of collective action based on key aspects of its effectiveness. Method: The tool is based on a theoretical model that followed from case studies identifying the conditions associated with quality of partnerships. The tool was developed by operationalizing these conditions into a series of statements, and pretested using the cognitive interviewing method. Construct validity and ecological validity were verified. Results: The tool includes 18 items, with 3 answer choices provided for each item. It is sensitive to variations in judgement. It allows for good convergence among respondents from participating organizations within a partnership; it can also distinguish between partnerships that have difficulty meeting certain conditions and those that do not. The tool is suitable for self-evaluation of partnerships engaged in common projects that involve more than information exchange. Discussion: The tool’s validity resides in its validation procedure and in the basic soundness of its theoretical model, which is supported by a number of literature reviews on how partnerships function and their results. Key words: Partnership; intersectoral action; actor network theory; questionnaire validation
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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 | 0.178 | 0.319 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".