Créer une communauté de pratique sur la recherche interventionnelle en santé mondiale
Bibliographic record
Abstract
INTRODUCTION: In the French-speaking world, specifically in France, intervention research in global health has yet to be fully developed institutionally. The Institute of Research for Development (IRD) is one of the major public actors in global health research in France. Within this institute, researchers publish and communicate little on intervention research despite the fact that this is part of their daily work. This is why, for the past several years, the health and society department of the IRD has been working towards institutionalizing a network of IRD actors in population health intervention research (PHIR). OBJECTIVE: The objective of this article is to analyze the needs of global health actors and elements that will allow for the construction of a community of practice in order to initiate an institutional anchoring of intervention research in global health through the mobilization of IRD actors. METHOD: Qualitative research was carried out in 2017 including individual and group interviews. The results yielded several observations: 1) a definition of PHIR that differs according to the participants, 2) a need to strengthen formal and informal interactions to respond to the need for training and sharing experiences, to reinforce encounters and interpersonal bonds, to increase communication and visibility of implemented actions, 3) the participants’ desire to evolve together to overcome certain inherent challenges of global health such as interdisciplinarity, North-South partnerships, or communication with different populations. CONCLUSION: Conducting population health intervention research requires a certain amount of reflection on the ways in which research is done and implies significant changes in the daily lives and work of researchers. It is essential to have institutional support to develop this, such as a community of practice. However, the absence of this community of practice three years later illustrates the operational challenges of implementing such an initiative.
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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 | 0.385 | 0.337 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.033 | 0.030 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.026 | 0.046 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".