Community first response and out-of-hospital cardiac arrest: a qualitative study of the views and experiences of international experts
Bibliographic record
Abstract
OBJECTIVES: This research aimed to examine the perspectives, experiences and practices of international experts in community first response: an intervention that entails the mobilisation of volunteers by the emergency medical services to respond to prehospital medical emergencies, particularly cardiac arrests, in their locality. DESIGN: This was a qualitative study in which semistructured interviews were conducted via teleconferencing. The data were analysed in accordance with an established thematic analysis procedure. SETTING: There were participants from 11 countries: UK, USA, Canada, Australia, New Zealand, Singapore, Ireland, Norway, Sweden, Denmark and the Netherlands. PARTICIPANTS: Sixteen individuals who held academic, clinical or managerial roles in the field of community first response were recruited. Maximum variation sampling targeted individuals who varied in terms of gender, occupation and country of employment. There were eight men and eight women. They included ambulance service chief executives, community first response programme managers and cardiac arrest registry managers. RESULTS: The findings provided insights on motivating and supporting community first response volunteers, as well as the impact of this intervention. First, volunteers can be motivated by 'bottom-up factors', particularly their characteristics or past experiences, as well as 'top-down factors', including culture and legislation. Second, providing ongoing support, especially feedback and psychological services, is considered important for maintaining volunteer well-being and engagement. Third, community first response can have a beneficial impact that extends not only to patients but also to their family, their community and to the volunteers themselves. CONCLUSIONS: The findings can inform the future development of community first response programmes, especially in terms of volunteer recruitment, training and support. The results also have implications for future research by highlighting that this intervention has important outcomes, beyond response times and patient survival, which should be measured, including the benefits for families, communities and volunteers.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".