RCT comparing the clinical effectiveness of conventional instructor-facilitated cardiac compression training to technology enhanced training using high-fidelity mannequins-A pilot study
Bibliographic record
Abstract
Introduction: Healthcare professionals often provide substandard chest compression following cardiac arrest. This is deemed a preventable harm because this skill can be acquired. The recent development of technology-enhanced cardiac compression training devices provides an alternative to traditional instructor-facilitated training. This pilot study compared the effectiveness of conventional and technology-enhanced training modalities.Methods: A pilot randomised controlled trial design was used in a regional hospital in Queensland. Following baseline assessment, healthcare staff were randomised to one of three groups: traditional instructor-facilitated training; high-fidelity mannequin training and continuous access to the training system to practise skills; and, high-fidelity mannequin training with no further access to the training system to practise skills. The primary outcome, cardiac compression skill levels, was analysed using analysis of co-variance, adjusting for predictive co-variates. Secondary measures were analysed using inferential statistics or presented descriptively.Results: Between January and February 2017, 502 healthcare staff were recruited. At baseline, 21\% were competent in cardiac compression, increasing to 38% on reassessment. The mode of training did not affect skill level (F(92,392) = 0.061, p = .94), however, participants in the high-fidelity mannequin training group who practised their skills had statistically significantly higher reassessment scores (z = -2.34, p = .019). Baseline score and the number of times participants practised their skills were significant predictors of reassessment scores (F(2,392) = 7.73, p = .001).Conclusions: Most hospital staff who may need to perform cardiac compression were not competent in this skill. Neither training modality was more effective. Both training and practise increased cardiac compression skill levels, indicating that frequent, low-dose training is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".