Co-creation within hybrid networks: what can be learnt from the difficulties encountered? The example of the fight against blood- and sexually-transmitted infections
Bibliographic record
Abstract
This article analyses co-creation processes within hybrid networks. Specifically, it looks at a particular co-creation mechanism, in this case, a strategic community set up to test new ways of dealing with blood- and sexually-transmitted infections in Quebec. A strategic community is a temporary structure of inter-organizational collaboration, made up of professionals, first-level managers, general practitioners, representatives of community organizations, etc. tasked with generating, implementing and evaluating new ideas about the organization of services. The results of this study highlight the difficulties encountered as well as the issues related to these co-creation processes. Notes for practitioners The implementation of co-creation processes, involving public, private and community actors, has to contend with numerous challenges: (1) the enrolment of the stakeholders concerned by the complex issues; (2) the creation of places for discussion and experimentation involving actors who play an important role in the implementation of change; (3) the mobilization of tools and facilitators to develop a common vision; (4) the action stage to validate new ways of doing things.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".