Optimizing Communication About Topical Corticosteroids: A Quality Improvement Study
Bibliographic record
Abstract
BACKGROUND: Patients are often non-adherent to topical corticosteroids (TCS). This may be in part due to poor communication between patients and dermatologists. OBJECTIVES: This quality improvement (QI) study aims to describe dermatologist-patient communication about TCS treatments and to compare communication before and after the implementation of an educational intervention. METHODS: This QI study assesses the communication between dermatologists and new dermatology outpatients receiving a TCS prescription in a tertiary care center. The QI intervention is 2-pronged, consisting of an educational pamphlet for patients and a communication workshop for the dermatology team. Encounters were audiotaped, and communication was analyzed using a coding system (MEDICODE). Phase 1 recordings happened preintervention and reflect the usual dermatologist-patient communication in this practice setting and phase 2 recordings were postintervention. RESULTS: Phase 1 reveals that dermatologists frequently address informational medication themes, such as naming the medications and informing patients about their proper use. They less frequently discuss patient experience themes, such as goals of treatment, adverse effects of treatments, and exploring patients' emotions about medications (such as anxiety, fears, etc.). After the intervention, there was more frequent discussion of patient experience themes without increasing consultation length. But, in both phases, physicians address most themes as a monolog with little verbal input from patients. CONCLUSIONS: Our study raises awareness regarding dermatologists' communication patterns about TCS, identifying specific areas for improvement, such as discussions of adverse effects, and explicitly addressing patients' attitudes and emotions. This is an essential step to foster a sense-making of TCS for patients.
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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 | 0.032 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".