How can community pharmacists be supported to manage skin conditions? A multistage stakeholder research prioritisation exercise
Bibliographic record
Abstract
OBJECTIVE: To establish research priorities which will support the development and delivery of community pharmacy initiatives for the management of skin conditions. DESIGN: An iterative, multistage stakeholder consultation consisting of online survey, participant workshops and prioritisation meeting. SETTING: All data collection took place online with participants completing a survey (delivered via the JISC Online Survey platform, between July 2021 and January 2022) and participating in online workshops and meetings (hosted on Microsoft Teams between April and July 2022). PARTICIPANTS: 174 community pharmacists and pharmacy staff completed the online survey.53 participants participated in the exploratory workshops (19 community pharmacists, 4 non-pharmacist members of pharmacy staff and 30 members of the public). 4 healthcare professionals who were unable to attend a workshop participated in a one-to-one interview.29 participants from the workshops took part in the prioritisation meeting (5 pharmacists/pharmacy staff, 1 other healthcare professional and 23 members of the public). RESULTS: Five broad areas of potential research need were identified in the online survey: (1) identifying and diagnosing skin conditions; (2) skin conditions in skin of colour; (3) when to refer skin conditions; (4) disease-specific concerns and (5) product-specific concerns.These were explored and refined in the workshops to establish 10 potential areas for research, which will support pharmacists in managing skin conditions. These were ranked in the prioritisation meeting. Among those prioritised were topics which consider how pharmacists work with other healthcare professionals to identify and manage skin conditions. CONCLUSIONS: Survey responses and stakeholder workshops all recognised the potential for community pharmacists to play an active role in the management of common skin conditions. Future research may support this in the generation of resources for pharmacists, in encouraging public take-up of pharmacy services, and in evaluating the most effective provision for dealing with skin conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".