When patients take the initiative to audio-record a clinical consultation
Bibliographic record
Abstract
OBJECTIVE: to get insight into healthcare professionals' current experience with, and views on consultation audio-recordings made on patients' initiative. METHOD: 215 Dutch healthcare professionals (123 physicians and 92 nurses) working in oncology care completed a survey inquiring their experiences and views. RESULTS: 71% of the respondents had experience with the consultation audio-recordings. Healthcare professionals who are in favour of the use of audio-recordings seem to embrace the evidence-based benefits for patients of listing back to a consultation again, and mention the positive influence on their patients. Opposing arguments relate to the belief that is confusing for patients or that it increases the chance that information is misinterpreted. Also the lack of control they have over the recording (fear for misuse), uncertainty about the medico-legal status, inhibiting influence on the communication process and feeling of distrust was mentioned. For almost one quarter of respondents these arguments and concerns were reason enough not to cooperate at all (9%), to cooperate only in certain cases (4%) or led to doubts about cooperation (9%). PRACTICE IMPLICATIONS: the many concerns that exist among healthcare professionals need to be tackled in order to increase transparency, as audio-recordings are expected to be used increasingly.
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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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".