Health-Related Coping and Social Interaction in People with Multiple Sclerosis Supported by a Social Network: Pilot Study With a New Methodological Approach
Bibliographic record
Abstract
BACKGROUND: Social media are a vital link for people with health concerns who find in Web communities a valid and comforting source for information exchange, debate, and knowledge enrichment. This aspect is important for people affected by chronic diseases like multiple sclerosis (MS), who are very well informed about the disease but are vulnerable to hopes of being cured or saved by therapies whose efficacy is not always scientifically proven. To improve health-related coping and social interaction for people with MS, we created an MS social network (SMsocialnetwork.com) with a medical team constantly online to intervene promptly when false or inappropriate medical information are shared. OBJECTIVE: The goal of this study was to assess the impact of SMsocialnetwork.com on the health-related coping and social interaction of people with MS by analyzing areas of interest through a Web-based survey. METHODS: Referring to previous marketing studies analyzing the online platform's role in targeted health care, we conducted a 39-item Web-based survey. We then performed a construct validation procedure using a factorial analysis, gathering together like items of the survey related to different areas of interest such as utility, proximity, sharing, interaction, solving uncertainty, suggestion attitude, and exploration. RESULTS: We collected 130 Web-based surveys. The areas of interest analysis demonstrated that the users positively evaluated SMsocialnetwork.com to obtain information, approach and solve problems, and to make decisions (utility: median 4.2); improve feeling of closeness (proximity: median 5); catalyze relationships and text general personal opinions (sharing: median 5.6); get in touch with other users to receive innovative, effective, and practical solutions (interaction, solving uncertainty, and suggestion attitude medians were respectively: 4.1, 3, and 3); and share information about innovative therapeutic approaches and treatment options (suggestion attitude: median: 3.3). CONCLUSIONS: SMsocialnetwork.com was perceived by users to be a useful tool to support health-related coping and social interaction, and may suggest a new kind of therapeutic alliance between physicians and people with MS.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".