Tailored Basic Life Support Training for Specific Layperson Populations—A Scoping Review
Bibliographic record
Abstract
Background: Basic life support (BLS) is a life-saving link in the out-of-hospital cardiac arrest chain of survival. Most members of the public are capable of providing BLS but are more likely to do so confidently and effectively if they undertake BLS training. Lay members of the public comprise diverse and specific populations and may benefit from tailored BLS training. Data on this topic are scarce, and it is completely unknown if there are any benefits arising from tailored courses or for whom course adaptations should be developed. Methods: The primary objective of this scoping review was to identify and describe differences in patient, clinical, and educational outcomes when comparing tailored versus standard BLS courses for specific layperson populations. This review was undertaken as part of the continuous evidence evaluation process of the International Liaison Committee on Resuscitation. Results: A primary search identified 1307 studies and after title, abstract, and full-text screening, we included eight publications reporting on tailored courses for specific populations. There were no studies reporting direct comparisons between tailored and standardized training. Seven (88%) studies investigated courses tailored for individuals with a disability, and only one study covered another specific population group (refugees). Overall, the quality of evidence was low as the studies did not compare tailored vs. non-tailored approaches or consisted of observational or pre–post-designed investigations. Conclusions: Tailored BLS education for specific populations is likely feasible and can include such groups into the pool of potential bystander resuscitation providers. Research into comparing tailored vs. standard courses, their cost-to-benefit ratio, how to best adapt courses, and how to involve members of the respective communities should be conducted. Additionally, tailored courses for first responders with and without a duty to respond could be explored.
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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".