Evaluating the Impact of an Educational Intervention on Documentation of Decision‐making Capacity in an Emergency Medical Services System
Bibliographic record
Abstract
OBJECTIVES: To compare the documentation of decision-making capacity by advanced life support (ALS) providers and signature acquisition before, one month after, and one year after an educational intervention. METHODS: The intervention comprised a one-and-a-half-hour module on assessment and documentation of decision-making capacity. Ambulance call reports were reviewed for all ALS calls occurring during three two-month periods, and refusals of transport were recorded. Provider compliance with documentation of decision-making capacity and signature acquisition were determined from a convenience sample of 75 reports from each period. Reviewers were blinded to study period. Twenty-percent double data entry was undertaken to evaluate accuracy. Ninety-five percent confidence intervals were calculated to compare frequencies of cancelled calls and documentation. RESULTS: From the emergency medical services database, 7,744 calls before the intervention, 7,444 immediately after, and 7,604 one year later were identified. Documentation rates in the second and third periods did not differ from that prior to the intervention (1.3% vs. 0.0% and 0.0% in subsequent periods), nor did the rates of signature acquisition differ (85.3% vs. 85.3% and 78.6%). The accuracy of data entry was 92.6%. However, the frequency of call refusals decreased significantly after the intervention (from 9.0% to 2.0% and 6.6% in the respective periods). CONCLUSIONS: An educational intervention resulted in no change in the rate of decision-making capacity documentation or signature acquisition by ALS providers for refusal of transport. There was a temporary increase in the number of transported patients.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".