Characteristics and Risk of Adverse Mental Health Events Amongst Users of the National Overdose Response Service (NORS) Telephone Hotline
Bibliographic record
Abstract
The National Overdose Response Service (NORS) is a Canadian mobile or virtual overdose response hotline intended to prevent drug overdose deaths but has unexpectedly received mental health related calls, including adverse mental health events. Our study aimed to examine these occurrences and caller characteristics predictive of adverse mental health outcomes. Using the NORS call dataset, we conducted a descriptive representation of mental health occurrences and mental health emergencies along with correlative statistics. We found that NORS had received 2518 mental health calls, with 28 (1.1%) being adverse events. Men, rural callers, polyroute substance consumption and history of overdosing were found to have increased odds of having an adverse mental health event, while being from Quebec, using non-standard consumption routes and using the line between 50 and 99 times were found to decrease odds. This supports the utility of overdose prevention hotlines to also support people experiencing adverse mental health situations and reduce harm for individuals with mental health and/or substance use disorders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".