What attracts patients with diabetes to an internet support group? A 21‐month longitudinal website study
Bibliographic record
Abstract
AIMS: To establish and evaluate a web-based educational and emotional resource for patients with diabetes and their family members. METHODS: A total of 47 365 user visits over a 21-month period to three internet discussion groups about diabetes were tracked for user activity, characteristics and level of satisfaction. RESULTS: The primary domains of users were the US (70%) and Canada (4%). Of all users, 7.55% posted messages, while 92.45% read messages posted by others. The average length of use was 15 min 5 s. Forty-four per cent posted messages to the nutrition discussion, 38% posted messages to the motivational discussion, and 18% posted messages to the family discussion. The most common postings addressed nutrition (42%), the emotional impact of diabetes (18%), managing high or low blood glucose levels (10%), and complications (8%). Respondents to the satisfaction survey were 64% female, 43% were insulin and 37% non-insulin users. Eighty-four per cent were older than 30 years, 34% had recently diagnosed diabetes and 32% had diabetes > 10 years. Forty-three per cent visited more than three times. Seventy-nine per cent of all respondents rated participation in the chat as having a positive effect on coping with diabetes. CONCLUSIONS: A professionally moderated internet discussion group is actively visited by a broad base of patients and families, and appears to be a useful strategy for engaging patients with chronic disease for emotional support and information exchange.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".