Personality, Attitudes, and Behaviors Predicting Perceived Benefit in Online Support Groups for Caregivers: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Online support groups (OSGs) are distance-delivered, easily accessible health interventions offering emotional, informational, and experience-based support and companionship or network support for caregivers managing chronic mental and physical health conditions. OBJECTIVE: This study aimed to examine the relative contribution of extraversion, agreeableness, neuroticism, positive attitudes toward OSGs on social networking sites, and typical past OSG use patterns in predicting perceived OSG benefit in an OSG for parents and caregivers of children with neurodevelopmental disorders. METHODS: A mixed methods, longitudinal design was used to collect data from 81 parents across Canada. Attitudes toward OSGs and typical OSG use patterns were assessed using the author-developed Attitudes Toward OSGs subscale (eg, "Online support groups are a place to get and give emotional support") and Past Behaviors in OSGs subscale (eg, "How often would you typically comment on posts?") administered at baseline-before OSG membership. The personality traits of extraversion, agreeableness, and neuroticism were assessed at baseline using the Ten-Item Personality Inventory. Perceived OSG benefit was assessed using the author-developed Perceived OSG Benefit scale (eg, "Overall, did you feel supported by other members in this group?"), administered 2 months after the initiation of OSG membership. RESULTS: =0.125; P<.001). CONCLUSIONS: The key suggestions for improving future OSGs were facilitating more in-depth, customized, and interactive content in OSGs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".