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Record W4411733547 · doi:10.1093/bjd/ljaf085.163

P135 Patient satisfaction in virtual support group meetings for vitiligo: a service evaluation

2025· article· en· W4411733547 on OpenAlexaff
Shahnawaz Towheed, Viktoria Eleftheriadou

Bibliographic record

VenueBritish Journal of Dermatology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVitiligoPatient satisfactionService (business)MedicineNursingDermatologyBusiness

Abstract

fetched live from OpenAlex

Abstract Vitiligo, a chronic skin condition characterized by depigmentation, has significant psychosocial impacts that extend beyond its cosmetic effects, often resulting in isolation, reduced self-esteem, and stigma. These challenges elucidate the need for supportive resources to improve quality of life. In response, a global virtual service was launched in 2023 under the auspices of an established advocacy organization to provide individuals with vitiligo a safe and inclusive environment for emotional connection and peer support. This service evaluation aimed to assess the effectiveness of these monthly virtual support group meetings in fostering satisfaction, comfort and engagement among patients in attendance. The sessions, lasting 1.5 h, are facilitated on a videoconferencing platform by volunteers with a lived experience of vitiligo. Every other month, sessions feature guest speakers such as psychologists, nutritionists and writers, who present expert insights and personal narratives to enrich discussions. Participants completed surveys after the session measuring satisfaction, feelings of safety and comfort, and likelihood of future attendance, using a five-point Likert scale (1 = least positive, 5 = most positive). Of the 35 survey responses analysed, quantitative feedback was overwhelmingly positive, with 33 participants (94%) rating their satisfaction and safety at the highest level. Similarly, 32 participants (91%) expressed a very high likelihood of attending future sessions. All respondents scored ≥ 4 on each scale, indicating no adverse experiences across the sample. Qualitative feedback, provided as open-ended text responses, was analysed thematically, revealing three key themes. Attendees highlighted the sessions’ ability to foster community and belonging (n = 14, 40%), emphasizing the welcoming and inclusive atmosphere. Emotional support and validation (n = 12, 34%) emerged as another theme, with respondents describing the sessions as safe spaces to share vulnerabilities and feel understood. Practical and informative content (n = 9, 26%) was also valued, with attendees appreciating actionable insights from guest speakers, including evidence-based protocols for self-care and diet. Regarding areas of improvement, it was suggested to extend the length of sessions to allow more time for story sharing, to incorporate interactive elements such as breakout discussions and question-and-answer opportunities, and to diversify topics to include managing vitiligo in the workplace, navigating social stigma and coping with associated autoimmune conditions. This evaluation underscores the value of virtual support group meetings as an accessible adjunct to medical care for vitiligo, aligning with clinician calls for accessible, tailored interventions to address moderate-to-severe psychological distress. High satisfaction scores validate this model, while the feedback highlights opportunities for growth, including expanding session formats and optimizing engagement. Future evaluations could monitor longitudinal impacts on quality of life and retention to refine the platform and further address the evolving needs of this patient population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.008
GPT teacher head0.274
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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