Can Implementing New Services Organization Models to Better Meet the Needs of Young People Bring About Practice Changes? Analysis of an Experiment in Québec
Bibliographic record
Abstract
The research question addressed in this article is: Can implementing new services organization models to better meet the needs of young people bring about practice changes? More precisely, we examine the effects of a new model called Aire Ouverte (AO) which is implemented gradually across Quebec since 2019. This new model involves public sector and community organizations. To grasp practices’ change, we use cultural historical activity theory (CHAT) and employ a qualitative approach. Beyond a precise description of work activities, we gained an inside view of how the actors involved represented their practice and context. Our results show that practice changes seen by actors are in line with the object of the intervention, that is, responding rapidly to the expressed needs of young people. The development of new tools, flexible functioning, strengthening of interprofessional and intersectoral collaboration, involvement of young people in decision-making, all should contribute to improving response to their needs. This being said, a critical look at practice changes reveals a challenge in aligning the design and objective of AO with the needs of some young people. We noted also a poor alignment of effective collaborative practices between levels of care and the practices sought from intersectoral collaboration.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".