A Validated Questionnaire to Evaluate Primary Care Pediatrician Satisfaction With the Use of Teledermatology
Bibliographic record
Abstract
Background Teledermatology (TD) is a branch of telemedicine focused on the evaluation of cutaneous lesions by dermatologists remotely, in order to avoid unnecessary in-person consults that could be otherwise resolved by this method, and to shorten the time required for prompt evaluation of cutaneous diseases. Objective This study aimed to create and validate a questionnaire to evaluate satisfaction with the use of TD among primary care pediatricians (PCPs) and to test the questionnaire in our health area before performing an intervention for the optimization of TD. Methods We first created a questionnaire based on previous publications. Then, an expert consultation was made before drafting the final version of the questionnaire. We tested it twice among pediatricians of different health areas, with a 1-month gap between both evaluations. Internal consistency, reproducibility, and validity of the questionnaire were evaluated. Finally, the validated questionnaire was tested among the PCPs of our health area, to analyze their responses. Results We registered 38 questionnaire responses. In all, 30 (78.9%) PCPs actively used TD several times within a month or a year; none of them used TD daily. Technical and health care quality of TD was mostly considered as good or very good. TD was regarded as similar or even better than face-to-face evaluation for most PCPs, whereas 7.9% (3/38) of PCPs thought TD was worse than conventional consults. Most PCPs considered TD as an effective, self-learning, and trustable tool, and 10.5% (4/38) of them identified that pictures captured by mobile phones were a barrier for its use, as it affects patient privacy. Technical problems, absence of exclusive devices for image taking, and delayed answers are some other barriers for TD that need to be overcome. Nonetheless, all PCPs were satisfied with TD, and all of them reported they would continue or start to use this tool. Conclusions TD has demonstrated to be an efficient tool, as it reduces waiting time and costs for dermatology evaluation, and it increases satisfaction among professionals. With our proposed questionnaire, we validated that quality, usability, efficacy, and satisfaction related to TD in our health area had a positive consideration among PCPs in general, but there still are barriers to overcome. Conflict of Interest None declared.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".