L’utilisation des technologies pour optimiser la formation des intervenants en santé mentale aux traitements fondés sur les données probantes : où en sommes-nous ?
Bibliographic record
Abstract
Massive training of mental health providers is one of the proposed means of improving access to evidence-based treatment for a variety of common mental disorders. While communication and knowledge dissemination technologies (videoconferencing, web platforms) can help make training more accessible in time and space, their contribution to the development of skills among providers needs to be carefully evaluated. Objectives Establish how technologies are used to optimize the training of practitioners and what are the effects of online training on the acquisition of clinical knowledge and skills. Method A quick review of the literature was conducted. In order to be included, studies had to concern online training intended for providers in active clinical practice, deal with the treatment of a common mental disorder or addiction, and include an objective measure of clinical knowledge or skills. The studies were analyzed and compared in terms of learning methods and activities incorporating digital technologies. Results Twenty studies were identified. Passive learning methods, involving little or no interactivity, are predominant in asynchronous mode online training (AOT), while activities that allow a high level of interaction with the trainer, such as role-playing or supervision, are found in synchronous mode training. Knowledge acquisition: AOTs appear to be effective in improving the knowledge of the trainees for at least six months. Classroom training or the addition of supervision does not produce learning outcomes that are superior to the AOT. Skill acquisition: No conclusions can be drawn about the effects of online, asynchronous, synchronous or blended training, as the results are mixed or even contradictory. Conclusion Considering the low methodological quality of the studies analyzed, which limits the nature and scope of the conclusions of this review, the results of the studies suggest that AOTs that include basic interactive technologies, such as online quizzes, can be a simple and effective way to improve clinical knowledge of mental health practitioners.
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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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".