Intention to Use Automated Diagnosis and Clinical Risk Perceptions Among First Contact Clinicians in Resource-Poor Settings: Questionnaire-Based Study Focusing on Acute Burns
Bibliographic record
Abstract
Background: Burn automated diagnosis may be instrumental for accurate and timely decision-making at point-of-care, helping to ensure that the right patients are triaged to burns centers. This is particularly important in resource-poor settings. Objective: We studied the intention of nonspecialized clinicians to engage in automated diagnosis in burn care as well as their perceptions toward clinical risks. Methods: A self-administered survey was used among a purposive sample of first contact clinicians (n=56) and burns specialists (n=35). The survey had 2 main parts: 1 measuring the intention to use automated diagnosis as per 7 constructs of the Automation Acceptance Model (yielding 8 hypotheses) and 1 on clinical risk perceptions (likelihood and severity of 7 risks). Structural Equation Modelling was used to test the hypotheses among first contact clinicians, and the Mann-Whitney U test was used to measure differences in risk perceptions between the two clinical groups. Results: Many first contact clinicians would intend to use automated diagnosis for burns should the technology be made available in their departments (41/56, 73%). The Automation Acceptance Model concepts contributed moderately to explain what the intention to use automated diagnosis rests on (R2=0.432), with 5 out of 8 hypotheses being supported. The intention to use automated diagnosis was associated with perceived usefulness but not with attitudes toward using it. Of the 7 risks studied, the 1 that was most often considered as high risk of occurring was that of complex burns not being recognized (n=23, 29%). The 2 groups differed significantly in their concern regarding both the likelihood of happening and the severity of 2 risks: the undermanagement of severe burns and the overmanagement of minor burns. Specifically, a larger proportion of first contact clinicians were more concerned than burns specialists (n=13, 27% versus 6% and n=11, 23% versus 6% for undermanagement and overmanagement, respectively). Conclusions: Almost three-quarters of first contact clinicians were inclined to seek automated advice for burn diagnosis. The proposed model contributes to explaining the intention to use with 5 hypotheses supported. When seeking additional determinants, clinical risk perception is a dimension that should be considered in any artificial intelligence implementation process, to help ensure sustainability.
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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 | 0.012 |
| 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 | 0.001 |
| 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".