Comparaison de trois stratégies de travail en réseau afin de favoriser l’application des connaissances issues de la recherche
Bibliographic record
Abstract
Abstract: In research as in practice, establishing strong teams is a recognized necessity and teams are increasingly large and dispersed. In the field of health care, the need for pluridisciplinarity is a further constraint in the organization of such teams or at least communication within them. Despite these difficulties, networking has emerged among groups of practitioners who share common objectives and try to fulfill their mission more effectively by developing, sharing, and applying knowledge targeted based on their fields of interest. This article is a critical analysis of three different networking experiences that all shared the common objective of application of knowledge through networking among practitioners: the suicide prevention practice community (CoP) of the Centre for Research and Intervention on Suicide and Euthanasia (CRISE), the task force on suicide in prisons of the International Association for Suicide Prevention (IASP), and the international Francophone network for safety promotion and trauma prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".