Online social support: Theoretical and methodological issues, social and health benefits, and recommendations
Bibliographic record
Abstract
ABSTRACT Online social support platforms (discussion forums, Facebook groups, chat rooms, etc.) are increasingly used by people living with chronic diseases and their caregivers, who aspire to exchange with people living with similar problems outside their traditional network. The objective of this literature review is to present online social support interventions described in recent scientific literature, to: 1) guide organizations that want to develop such intervention or improve an existing program, and 2) identify research avenues for researchers and recommendations for health planners. Some 59 scientific articles presenting online social support interventions (2006-2016) were analyzed using a grid emphasizing the theoretical conceptions of social support, the web platforms used and their functionalities, the design process and evaluation of the interventions, the methods of participation and animation set up by the organizations, the documented impacts of the interventions on the populations, and finally the lines of research and the recommendations for the field planners. A narrative methodology was used to highlight development and implementation challenges to support our partner organizations in developing or improving their online social support interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.112 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".