Physician experience with speech recognition software in psychiatry: usage and perspective
Bibliographic record
Abstract
OBJECTIVE: The purpose of this paper is to extend a previous study by evaluating the use of a speech recognition software in a clinical psychiatry milieu. Physicians (n = 55) at a psychiatric hospital participated in a limited implementation and were provided with training, licenses, and relevant devices. Post-implementation usage data was collected via the software. Additionally, a post-implementation survey was distributed 5 months after the technology was introduced. RESULTS: In the first month, 45 out of 51 (88%) physicians were active users of the technology; however, after the full evaluation period only 53% were still active. The average active user minutes and the average active user lines dictated per month remained consistent throughout the evaluation. The use of speech recognition software within a psychiatric setting is of value to some physicians. Our results indicate a post-implementation reduction in adoption, with stable usage for physicians who remained active users. Future studies to identify characteristics of users and/or technology that contribute to ongoing use would be of value.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".