Effect of low dose high frequency training on paramedic cognitive skills
Bibliographic record
Abstract
Background: Paramedics in many parts of the US are required to obtain advanced cardiac life support (ACLS) recertification every two years. However, like other healthcare providers, they may experience problems with retention of this knowledge. Study objectives: This year-long study examined the difference in ACLS cognitive performance, measured by a modified Megacode, between two groups of paramedics: those who practiced for 10 minutes monthly over 10 months using brief computer-based ACLS scenarios, and those who did not refresh. Methods: Participants were randomised into the experimental group using computer gaming for a minimum of 10 minutes a month, and a control group that did not. In month 12, all participants took a post-test Megacode. Results: 27 (79%) of the experimental and 18 (95%) of the control group successfully completed the pre-test Megacode. 38 (72%) of all participants passed both the pre- and post-test Megacodes; three (6%) failed both Megacodes, five (9%) of the experimental group who failed the pretest passed the post-test at month 12. Four participants in the experimental group and three in the control group failed the post-test at month 12. Conclusions: paramedics recalled ACLS algorithms with or without practice.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".