What Is Known About the Psychodermatology Clinic Model of Care? A Systematic Scoping Review
Bibliographic record
Abstract
BACKGROUND: Psychodermatology focuses on the interaction between skin and mental health. Existing research discusses the recognition and treatment of these disorders. However, little is known about the operational structure of subspecialised psychodermatology clinics. OBJECTIVE: To identify literature on the structure and logistics of delivering a psychodermatology service. METHODS: A systematic search of MEDLINE, PsycINFO, Embase, and Google Scholar was performed. Articles were included if they discussed the concept and organisation of a psychodermatology practice. RESULTS: We identified 693 studies; after screening titles and abstracts, 35 full-text articles were assessed, and 17 were included in the scoping review. Most articles discussed aspects of clinic organisation in general; others discussed management of a clinic in the context of specific diseases or made recommendations on incorporating psychotherapeutic techniques in a solo practitioner setting. A weekly multidisciplinary clinic or resident teaching clinic with joint dermatologist-psychiatrist consultation is the most commonly reported model. Specifically, a stepped level of care approach is often used, where patients in increasing level of distress are stratified to the appropriate team of trained professionals. A corresponding curriculum to supplement practitioners' knowledge is recommended. CONCLUSIONS: Various clinic models have been described to provide specialised psychodermatology care in specific settings. Research is needed to assess the impact of these multidisciplinary models of care on patient outcomes and health care costs.
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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 | 0.023 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".