Medical dispatch decision support for transfer time estimation: Individual operator differences in system use
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medical dispatchers use estimates of patient transfer times to inform dispatch decisions, and decision support tools that assist with time estimation may lead to improved patient outcomes. However, individual differences between medical dispatchers may result in variances in how these tools are used in practice. A study was conducted to explore how individual difference factors such as numeracy ability, impulsiveness, and venturesomeness are associated with different time prediction strategies when using decision support tools that display historical transfer time information. It was found that individuals did exhibit different time prediction strategies, and some of the variance in behavior could be explained by differences in numeracy and impulsiveness. These preliminary results suggest caution when designing support tools, especially when the target population has large variability in terms of numeracy and impulsiveness characteristics.
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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 | 0.000 | 0.000 |
| 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