Contributions et défis de l’utilisation des technopédagogies à des fins de soutien à l’appropriation des meilleures pratiques en santé mentale
Bibliographic record
Abstract
Context Despite the considerable resources devoted and the efforts of the many actors involved, the gap between the production of scientific knowledge and its use in practice remains a challenge. The use of information and communication technologies (ICTs) is a valuable tool for reducing this gap. To address this challenge, a demonstration project focusing on the use of technology for knowledge translation was implemented with 23 community support teams in 5 regions of Quebec (2016-2018). More than 324 mental health professionals, team leaders and managers have benefited from the initiative "At your fingertips, best recovery-oriented practices." Objective This article presents the results of a satisfaction survey of team leaders responsible for clinical support in the community support teams under study. The purpose of this study is to enhance the understanding of issues identified during implementation and to make recommendations for the sustainable scaling up of the implemented knowledge translation program. Method A qualitative design in this evaluative research was adopted. At the end of the program implementation process, 2 group interviews were conducted with the team leaders. A content analysis following an inductive approach with 3 levels of coding was performed. Results The results show a significant digital gap within the Quebec health and social services network compared to other sectors of activity. Participants underlined the importance of adopting mechanisms for knowledge exchange and transfer that are integrated into organizational practices (dedicated time, formal clinical supervision, etc.) and which make use of ICTs. Conclusion Despite the significant technology upgrade required, the results suggest the relevance of using techno-pedagogy as the primary means of supporting knowledge translation and practice transformation. The tools developed and the support mechanisms explored appear to facilitate access to and adoption of best practices in mental health.
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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 | 0.037 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".