“I Am a Quarterback”: A Mixed-Methods Study of Death Investigators’ Communication with Family Members of Young Sudden Cardiac Death Victims
Bibliographic record
Abstract
Background: Sudden cardiac death (SCD) is a leading global cause of mortality and is often attributable to heritable cardiac conditions in the young (aged 2-45 years). Death investigators are responsible for determining the cause of death and communicating the risk of heritable conditions with the families of victims. Families often struggle to comprehend this information. This study explores how death investigators communicate with families of young SCD victims who die from suspected heritable causes about the cause of death and their risk for SCD. Methods: We conducted an explanatory sequential mixed-methods study, collecting quantitative data, via a web-based survey, and qualitative data, via telephone interviews, to investigate how death investigators in Ontario and Nova Scotia, Canada, communicate with family members of SCD victims. We used descriptive statistics to analyze quantitative data, and thematic analysis to analyze qualitative data. We triangulated the data at multiple levels. Results: Between October 2022 and July 2023, we surveyed 78 death investigators and interviewed a subset of these (n = 20). Participants (40%; n = 31) reported that SCDs due to heritable cardiac conditions were more difficult to investigate, requiring a higher frequency of communication with families. Participants (96.1%; n = 75) reported contacting family members via phone. Strategies to achieve their communication goals were influenced by family characteristics; involvement of other professionals; characteristics of the investigation, access to resources, and system-level barriers. Conclusions: SCD investigations in the young who die due to suspected heritable cardiac conditions are challenging and require a high frequency of communication. Further research should examine how systematic changes can improve communication among death investigators and families.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".