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
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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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".