Identification of Behavioral, Clinical, and Psychological Antecedents of Acute Stimulant Poisoning: Development and Implementation of a Mixed Methods Psychological Autopsy Study
Bibliographic record
Abstract
Background: Despite increasing fatal stimulant poisoning in the United States, little is understood about the mechanism of death. The psychological autopsy (PA) has long been used to distinguish the manner of death in equivocal cases, including opioid overdose, but has not been used to explicitly explore stimulant mortality. Objective: We aimed to develop and implement a large PA study to identify antecedents of fatal stimulant poisoning, seeking to maximize data gathering and ethical interactions during the collateral interviews. Methods: We ascertained death records from the California Electronic Death Reporting System (CA-EDRS) and the San Francisco Office of the County Medical Examiner (OCME) from June 2022 through December 2023. We selected deaths determined to be due to acute poisoning from cocaine or methamphetamine, which occurred 3-12 months prior and were not attributed to suicide or homicide. We identified 31 stimulant-fentanyl and 70 stimulant-no-opioid decedents. We sought 2 informants for each decedent, who were able to describe the decedent across their life course. Informants were at least 18 years of age, communicated with the decedent within the year before death, and were aware that the decedent had been using substances during that year. Upon completion of at least one informant interview conducted by staff with bachelor's or master's degrees, we collected OCME, medical record, and substance use disorder treatment data for the decedent. Planned analyses include least absolute shrinkage and selection operator regressions of quantitative data and thematic analyses of qualitative data. Results: We identified and interviewed at least one informant (N=141) for each decedent (N=101). Based on feedback during recruitment, we adapted language to improve rapport, including changing the term "accidental death" to "premature death," offering condolences, and providing content warnings. As expected, family members were able to provide more data about the decedent's childhood and adolescence, and nonfamily informants provided more data regarding events proximal to death. We found that the interviews were stressful for both the interviewee and interviewer, especially when participants thought the study was intrusive or experienced significant grief during the interviews. Conclusions: In developing and implementing PA research on fatal stimulant poisoning, we noted the importance of recruitment language regarding cause of death and condolences with collateral informants. Compassion and respect were critical to facilitate the interview process and maintain an ethical framework. We discuss several barriers to success and lessons learned while conducting PA interviews, as well as recommendations for future PA studies.
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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 | 0.064 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".