Community first response and out-of-hospital cardiac arrest: Identifying priorities for data collection, analysis, and use via the nominal group technique
Bibliographic record
Abstract
AIM: Community First Response (CFR) is an important component of Out-of-hospital Cardiac Arrest management in many countries, including Ireland. Reliable, strategic data collection and analysis are required to support the development of CFR. However, data on CFR are currently limited in Ireland and internationally. This research aimed to identify the most important CFR data to record, the most important uses of CFR data, and barriers and facilitators to CFR data collection and use. METHODS: The Nominal Group Technique structured consensus process was used. An expert panel comprising key stakeholders, including volunteers, clinicians, researchers, policy-makers, and a patient, completed a survey to generate lists of the most important CFR data to record and the most important uses of CFR data. Subsequently, they participated in a consensus meeting to agree the top ten priorities from each list. They also identified barriers and facilitators to CFR data collection and use. RESULTS: The top ten CFR data items to record included volunteer response time, interventions/activities completed by volunteers, and the mental/physical impact on volunteers. The top ten most important uses of CFR data included providing feedback to volunteers, improving volunteer training, and measuring CFR effectiveness. Barriers included time constraints and limited training. Facilitators included having appropriate software/equipment and collecting minimal data. CONCLUSION: The results can guide CFR research and inform the development of CFR data collection and analysis policy and practice in Ireland and internationally. Ultimately, improving CFR data collection and use will help to optimise this important intervention and enhance its evidence base.
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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 | 0.319 | 0.500 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".