Abstract 10244: The Long-Term Residual Chest Compression Performance Following a Novel Chest Compression Training: A Cohort Study
Bibliographic record
Abstract
Introduction: The Timely Chest Compression Training (T-CCT), a 20-minutes in-situ intervention, was created locally to ensure frequent training in chest compressions for personal support workers (PSW). While it was effective in improving chest compression performance immediately after training, skills retention remained unknown. Method: A retrospective cohort study was conducted in a tertiary university-affiliated hospital in Quebec, Canada. Chest compression performance scores were measured with manikins and a subtractive scoring model (Laerdal QCPR®). PSW were invited to participate in a T-CCT. At baseline before receiving any new training, we measured the proportion of PSW that had an “excellent” score, defined as a score greater or equal to 90 out of 100. We compared the baseline performance of PSW who received the T-CCT 10 months prior with those who did not receive it. Results: In total, 404 PSW were included. Of these, 229 (55%) had received the T-CCT before and 175 (43%) had not. Close to half of PSW who had received the T-CCT met the excellent performance threshold (46 % [106/229]), as opposed to 30 % (53/175) in those who had not received the intervention 10 months prior. In a univariable log-binomial model, being previously exposed to the T-CCT increased by 1.53 times (RR 1.53, 95%CI [1.17-1.99]) the risk of having an excellent chest compression score. When adjusted for covariables (i.e., age, experience, sex, and belonging to a critical care unit), the effect of the intervention remained (RR 1.57, 95%CI [1.19-2.07]). Conclusions: Although the T-CCT is a novel chest compression training intervention, our results suggest that acquired performance is sustained over time. Further research is needed to identify the optimal interval between these training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".