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Record W4380481253 · doi:10.2196/46682

General Practitioners’ Perspectives About Remote Dermatology Care During the COVID-19 Pandemic in the Netherlands: Questionnaire-Based Study

2023· article· en· W4380481253 on OpenAlexaffvenue
Esmée Tensen, Craig Kuziemsky, Monique Jaspers, Linda Peute

Bibliographic record

VenueJMIR Dermatology · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsMacEwan University
Fundersnot available
KeywordsPandemicTeledermatologyTelemedicineWorkloadComputer-assisted web interviewingSociotechnical systemService (business)MedicineSocial distanceCoronavirus disease 2019 (COVID-19)TelehealthFamily medicineTriageHealth careMedical educationNursingMedical emergencyComputer scienceBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic affected the delivery of primary care and stimulated the use of digital health solutions such as remote digital dermatology care. In the Netherlands, remote store-and-forward dermatology care was already integrated into Dutch general practice before the COVID-19 pandemic. However, it is unclear how general practitioners (GPs) experienced this existing digital dermatology care during the pandemic period. OBJECTIVE: We investigated GPs' perspectives about facilitators and barriers related to store-and-forward digital dermatology care during the COVID-19 pandemic in the Netherlands, using a sociotechnical approach. METHODS: In December 2021, a web-based questionnaire was distributed via email to approximately 3257 GPs who could perform a digital dermatology consultation and who had started a digital consultation (not necessarily dermatology) in the previous 2 years. The questionnaire consisted of general background questions, questions from a previously validated telemedicine service user satisfaction questionnaire, and newly added questions related to the pandemic and use of the digital dermatology service in general practice. The open-ended and free-text responses were analyzed for facilitators and barriers using content analysis, guided by an 8-dimensional sociotechnical model. RESULTS: In total, 71 GPs completed the entire questionnaire, and 66 (93%) questionnaires were included in the data analysis. During the questionnaire distribution period, another national lockdown, social distancing, and stay-at-home mandates were announced; thus, GPs may have had increased workload and limited time to complete the questionnaire. Of the 66 responding GPs, 36 (55%) were female, 25 (38%) were aged 35-44 years, 33 (50%) were weekly platform users, 34 (52%) were working with the telemedicine organization for >5 years, 42 (64%) reported that they used the store-and-forward platform as often during as before the pandemic, 61 (92%) would use the platform again, 53 (80%) would recommend the platform to a colleague, and 10 (15%) used digital dermatology home consultation. Although GPs were generally satisfied with the digital dermatology service, platform, and telemedicine organization, they also experienced crucial barriers to the use of the service during the pandemic. These barriers were GPs' and patients' limited digital photography skills, costs and the lack of appropriate equipment, human-computer interface and interoperability issues on the telemedicine platform, and different use procedures of the digital dermatology service. CONCLUSIONS: Although remote dermatology care was already integrated into Dutch GP practice before the pandemic, which may have facilitated the positive responses of GPs about the use of the service, barriers impeded the full potential of its use during the pandemic. Training is needed to improve the use of equipment and quality of (dermoscopy) images taken by GPs and to inform GPs in which circumstances they can or cannot use digital dermatology. Furthermore, the dermatology platform should be improved to also guide patients in taking photographs with sufficient quality.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.407
Teacher spread0.364 · 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 teacher head, 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".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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