Predictors of overdose response hotline use for mental health and fatal overdose prevention
Bibliographic record
Abstract
OBJECTIVES: The overdose crisis remains one of the largest public health issues facing North America to date. Formalized virtual spotting services have gained popularity as a harm reduction intervention, proving early effectiveness in reducing overdose mortality. This study determined the characteristics of individuals who recurrently use one such service, Canada's National Overdose Response Service (NORS). METHODS: In this retrospective study, call logs from NORS were analyzed from service inception. Demographics including age, gender, province, community size, substance used, routes of administration, and adverse events were all collected and imputed into a marginal means and rates model to determine the predictors of recurrent service use. RESULTS: A total of 7340 unique calls were included within our analysis. Of those, 1167 (15.8%) reported their gender as male, 3744 (51.0%) as female, and 1329 (18.1%) as gender diverse, and 1100 (14.9%) did not report their gender. In terms of age, 46 (0.6%) were individuals under the age of 18 years, 3561 (48.5%) were between 18 and 30, 557 (7.6%) were between 31 and 40, 2505 (34.1%) were between 41 and 50, 525 (7.1%) were age 51 or over, and 146 (2.0%) did not report their age. Men's rate ratios for recurrent calls were significantly lower than women's (RR = 0.08, 95% CI = 0.07‒0.09), as were those for respondents aged 31‒40 years as compared with those aged 18‒30 (RR = 0.26, 95% CI = 0.15‒0.45). Between regions, rate ratios for callers from British Columbia (RR = 0.28, 95% CI = 0.17‒2.24) and Atlantic provinces (RR = 0.09; 95% CI = 0.07‒0.12) were significantly lower than those for callers from the province of Ontario. Similarly, rural callers demonstrated lower recurrent service use (RR = 0.08; 95% CI = 0.07‒0.11) than their urban counterparts. CONCLUSION: NORS demonstrates higher usage patterns within certain demographic groups, in particular, urban women. The results can therefore be used to target public health messaging toward those who derive the most benefit from the service and to tailor programming to those who are at highest risk to use alone.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".