End-users’ satisfaction and adoption regarding the implementation of a technology solution for screening and counselling individuals with suspicious COVID-19: a cross-section study
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Purpose We evaluated the end-users’ satisfaction and the adoption of a technology solution embedding a clinical decision algorithm for screening and counselling individuals with suspicious COVID-19. Methods This was a cross-sectional study. Data was collected by the startup company Hi! Healthcare Intelligence . Satisfaction was measured using two questions presenting answer options as Likert scales of eleven points (from 0 to 10), in which 0 indicated low satisfaction and 10 indicated high satisfaction. We measured ‘general satisfaction’ through the average of questions 1 and 2. Descriptive analyses were used to summarize the data. Results The average satisfaction regarding the experience in using the technology solution and regarding the ‘recommendation for a friend or family’ was 7.94 (95% confidence interval [CI] 7.60 to 8.28) and 8.14 (95% CI 7.80 to 8.48), respectively. ‘General satisfaction’ was 8.04 (95% CI 7.70 to 8.37). The adoption regarding the implementation of the technology solution was 24.5% ( n = 265). Conclusion The technology solution embedding a clinical decision algorithm for screening and counselling individuals with suspicious COVID-19 presented high satisfaction. One in four (¼) individuals interested in using the technology solution actually adopted it by following the clinical decision algorithm until the end, when counselling was provided.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it