Patients’ Experiences of Telemedicine for Their Skin Problems: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Teledermatology is a cost-effective treatment modality for the management of skin disorders. Most evaluations use quantitative data, and far less is understood about the patients' experience. OBJECTIVE: This qualitative study aimed to explore patients' perceptions of a teledermatology service linking public primary care clinics to the national specialist dermatology clinic in Singapore. A better understanding of patients' experiences can help refine and develop the care provided. METHODS: Semistructured in-depth interviews were conducted with patients who had been referred to the teledermatology service. The interviews were digitally recorded and transcribed before undergoing thematic content analysis. RESULTS: A total of 21 patients aged between 22 and 72 years were recruited. The following 3 themes were identified from the data of patients' experiences: positive perceptions of teledermatology, concerns about teledermatology, and ideas for improving the teledermatology service. The patients found the teledermatology service convenient, saving them time and expense and liberating them from the stresses incurred when making an in-person visit to a specialist facility. They valued the confidence and reassurance they gained from having a dermatologist involved in deciding their management. The patients' concern included data security and the quality of the images shared. Nonetheless, they were keen to see the service expanded beyond the polyclinics. Their experiences and perceptions will inform future service refinement and development. CONCLUSIONS: This narrative exploration of users' experiences of teledermatology produced rich data enabling a better understanding of the patients' journey, the way they understand and interpret their experiences, and ideas for service refinement. Telemedicine reduces traveling and enables safe distancing, factors that are much needed during pandemics.
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 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".