Effectiveness of Support Groups for Patients With Contact Dermatitis
Bibliographic record
Abstract
BACKGROUND: Contact dermatitis can be difficult to manage and overwhelming for patients, often requiring significant lifestyle changes. OBJECTIVE: The aim of the study was to investigate whether a contact dermatitis support group could help patients find community and learn from others who share similar experiences. METHODS: Hour-long, monthly support group meetings facilitated by a social worker, research fellows, and a faculty dermatologist were held for approximately 1.5 years. A 32-question, cross-sectional survey was administered to assess perception of contact dermatitis and overall usefulness of the group. RESULTS: Between 2 and 5 patients attended each group session; 9 participants completed the survey. Most were female (77.8%) and white (77.8%), with an average age of 68.8 years. Eight participants (88.9%) reported that it was important or somewhat important to socialize with others affected by contact dermatitis. Three group members (33.3%) had met with attendees outside of the monthly sessions. The majority (77.8%) reported that the support group had a positive effect on their understanding of contact dermatitis and would recommend the group to others (88.9%). CONCLUSIONS: Support groups may be helpful for patients learning to cope with the challenges associated with contact dermatitis. Although preliminary feedback is promising, further investigation is warranted to determine whether these groups are effective on a larger scale.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".