User Satisfaction with Asynchronous Telemedicine: A Study of Users of Santa Catarina's System of Telemedicine and Telehealth
Bibliographic record
Abstract
User satisfaction analyses in synchronous telemedicine and teleconsultation environments have been widely performed and generally show satisfied users. In the field of asynchronous telemedicine, however, satisfaction studies were performed only in one single location or with a restricted set of users. With the aim of offering an exemplar evaluation of the impact of the statewide use of a large-scale asynchronous telemedicine network on the satisfaction of the involved users, this study presents the results obtained from a survey of the perceived quality of the service by both patients and healthcare staff. For this purpose, a survey with satisfaction questionnaires was performed with 564 patients from seven upstate municipalities and 56 healthcare professionals from 46 municipalities, using a methodology from the process improvement field. The collected data were quantified and underwent statistical analysis, which showed a clear perception of the improvement in the quality of service by both patients and healthcare professionals. The present findings also showed that both patients and healthcare professionals felt that introducing these new technologies was a positive step, even in upstate areas and when they involved great changes in the usual processes of primary care.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".