Investigation of the Effects of an Online Support Group for Mental Health Problems on Stigma and Help-Seeking Among Japanese Adults: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Online support groups vary widely in both goals and structures owing to the rapid development of social networking services. Several studies have shown the potential effectiveness of online support groups, such as reducing psychological distress (eg, depression) among individuals with mental health problems. However, online support groups often do not aim at effectiveness regarding distress relief-related outcomes. OBJECTIVE: This study aims to examine whether the use frequency of online support group platform functions (U2plus) is associated with lower stigma and higher consumer activation. METHODS: A total of 350 U2plus users participated in a web-based survey. They were asked what therapy they had received in the past and how often they logged on to it, used each of its functions, and completed the following questionnaires: the Patient Health Questionnaire-9, the Devaluation-Discrimination Scale, and the General Help-Seeking Questionnaire. RESULTS: Regarding the therapy received, 88% (308/350) of participants had taken medication for mental health problems, and 66.6% (233/350) had received psychotherapy or mental health counseling. Regarding use frequency, 21.7% (74/341) of the participants signed in to U2plus and used its functions more than once a week. The use frequency of U2plus functions was not correlated with perceived stigma, but the use frequency of some functions was weakly correlated with help-seeking intentions from formal sources (eg, doctors and psychologists). However, multiple regression analyses revealed that the use frequency of those functions did not uniquely predict help-seeking intentions. CONCLUSIONS: It was suggested that online support groups may serve as an alternative treatment option for those who are already undergoing pharmacological treatment and are willing to seek help from whatever source they deem helpful.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".