27 Lay responder post arrest support model: methodology & conceptual design
Bibliographic record
Abstract
Aim As early as 1993, consideration of the psychological effect of providing CPR on bystanders emerged as an underappreciated concern. One consideration is the ethics of asking people to respond to such emergencies without proper support. Method The Lay Responder Support Model (LRSM) emerged from the analysis of the data collected after debriefings with 64 lay-responders that participated in an out-of-hospital cardiac arrest. During the first conversations, participants identified the effects of mental trauma, which led to formalise the debriefing process and data collection tools. The program now involves 3 stages: Identifying and Engaging, Debriefing and Follow-up, and Referral for Professional Support. Results Almost all the cases, lay-responders communicated effects in their daily life, including a wide range of acute physical and/or psychological reactions post event. For some individuals, acute stress reactions caused enough distress to interfere with everyday activities. These findings resulted in the application of Psychological First Aid principles: identifying and facilitating them toward mental health support to promote recovery has wide spread application in traumatic events like disasters. The LRSM design now supports engagement with lay responders very early in post-event period, and informed by continual findings. Conclusion The LRSM provides a structured framework to capture information about witnessing a SCA from the lay-responders involved the role they played, actual clinical records, and to identify areas of support for lay-responder’s residual mental health. It potentially goes beyond cardiac arrest situations and may prove helpful to psychological first aid providers and other public health organisations identifying and referring people to appropriate resources. Conflict of interest None Funding Employer – Peel Regional Paramedic Services.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".