Navigating cardiac arrest together: A survivor and family-led co-design study of family needs and care touchpoints
Bibliographic record
Abstract
Introduction: This study aimed to i) identify the care needs of families experiencing cardiac arrest; and ii) co-identify strategies for meeting the identified care needs. Cardiac arrest survivors and family members (of survivors and non-survivors) were engaged as "experience experts," collaborators and co-researchers in this study. Methods: A qualitative study using semi-structured interviews of cardiac arrest survivors and family members was conducted. Participants were recruited from the membership of the Family Centred Cardiac Arrest Care Project. Interviews were recorded, transcribed, and analysed using Framework analysis. Results: Twenty-eight participants described 22 unique cardiac arrest events. We identified five primary care need themes: 1) "Help us help our loved one"; 2) "Work with us as a cohesive team"; 3) "See us: treat us with humanity and dignity"; 4) "Address our family's ongoing emergency"; and 5) "Help us to heal after the cardiac arrest" as well as 29 subordinate care need themes. We performed touchpoint mapping to identify key moments of interaction between patients and families, and the health system to highlight potential areas for improvement, as well as strategies for meeting family care needs. Conclusion: Our participants identified varied family care needs during and long after cardiac arrest. Fortunately, many proposed strategies are inexpensive and have low barriers to adoption. However, some unmet care needs identified suggest larger systemic issues such as service gaps that leave families feeling abandoned and isolated. Overall, our findings suggest that care during and after cardiac arrest are critical components of a comprehensive cardiac arrest care system.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".