Le soutien social en ligne comme mode d’intervention psychosociale : revue de littérature, pistes de recherche et recommandations pour les intervenants
Bibliographic record
Abstract
OBJECTIVE: The goal of this review is to present online social support interventions described in recent scientific literature, in order to (i) guide organizations wishing to develop such an intervention or to improve an existing program, and (ii) to identify future research directions and recommendations for practitioners. METHODS AND RESULTS: 59 peer-reviewed articles presenting online social support interventions (2006-2016) were analyzed by using a thematic grid focusing on theoretical perspectives on social support, the online platforms used and their functionalities, the process of intervention development and evaluation, the modalities of participation and the facilitation methods, the documented impacts of interventions, and finally future research directions and recommendations for practitioners. A narrative methodology was used to identify challenges in intervention development and implementation, in order to provide guidance to organizations who want to develop or improve their online social support services. CONCLUSIONS: Several research directions and recommendations for the development of online social support interventions are suggested, including the need to develop theoretical models of online social support and enrich traditional models of social support, the need to understand the benefits associated with different levels of participation, the importance of needs assessment in the development of interventions, and the contribution of qualitative methods to the evaluation of interventions.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".