The experience of online cardiac arrest video use for education and research: A qualitative interview study completed in partnership with survivors and co-survivors
Bibliographic record
Abstract
Background: Swift recognition of cardiac arrest is required for survival, however failure to recognize (and delayed response) is common. Studying online cardiac arrest videos may aid recognition, however the ethical implications of this are unknown. We examined their use from the perspective of persons with lived experience of cardiac arrest, seeking to understand the experience of having one's cardiac arrest recorded and available online. Methods: We gathered qualitative data using focused interviews of persons affected by cardiac arrest. Inductive thematic analysis was performed, as well as a deductive ethical analysis. Co-researcher survivors and co-survivors were involved in all stages of this project. Findings: We identified themes of 'shock, hurt and helplessness' and 'surreality and reality' to describe the experience of having one's (or a family member's) cardiac arrest captured and distributed online. Participants provided guidance on the use of online videos for education and research, emphasising beneficence, autonomy, non-maleficence, and justice. Conclusions: Finding one's own, or a family member's cardiac arrest video online is shocking and potentially harmful for families. If ethical principles are followed however, there may be acceptable procedures for the use of online videos of cardiac arrest for education or research purposes. The careful use of online videos of cardiac arrest for education and research may help improve recognition and response, though additional research is required to confirm or refute this claim.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".