Characteristics and use of services of accidental overdose victims in a semi-urban region of Quebec
Bibliographic record
Abstract
Abstract Background The overdose crisis represents a major public health issue in Canada. In the Monteregie region (Province of Quebec), the Coroner's Office reports numerous deaths caused by suspected drugs poisoning. By better understanding the characteristics of victims and their use of health services, recommendations can be proposed to improve interventions to prevent overdose deaths. Methods A retrospective descriptive design in two subsequent parts was used: 1) Based on coroner reports concerning accidental overdose deaths occurring between 2018 and 2022 among adults with an address associated with a primary residence in Monteregie (n = 222); 2) From medical records of a convenience sample of the first part (n = 34). Data extraction tools were developed and then validated. Descriptive and qualitative analyses were conducted. Results Accidental overdose death rates are higher in certain municipalities or communities. Witnesses to the intoxication are often present (45%). Problems with substance use (82%), physical health (54%) and mental health (46%) are frequently reported. The toxicological analyses show the presence of opioids (53%), benzodiazepines (39%), antidepressants (39%), antipsychotics (26%) and alcohol (24%). Half of the people had contact with the health and social services network before their death. Seven service use profiles were identified, including precariousness (26%), physical disorders (26%) and psychiatric disorders (18%). Conclusions Causes of higher overdose death rates should be further investigated so that targeted actions can be implemented. More measures to educate witnesses on how to recognize the signs and symptoms of overdose and how to intervene quickly by following best practices are needed. Interventions should target not only opioid use, but also prescription drugs and alcohol. The significant use of services before death and the specific profiles suggest opportunities for intervention related to the different comorbidities present. Key messages • A better understanding of the phenomenon of fatal accidental overdoses, using coroner reports and medical records, makes it possible to identify potential interventions to prevent overdoses. • Victims of accidental overdoses have individual characteristics and specific use of health services allowing for upstream interventions based on the different comorbidities present.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".