Determining the feasibility for an overdose prevention line to support substance users who use alone
Bibliographic record
Abstract
INTRODUCTION: The majority of opioid-related deaths occur in suburban communities with people who use alone in their homes. BACKGROUND: To reach individuals who use substances alone, Grenfell Ministries, a not for profit agency in Hamilton Ontario created a phone-based supervision service to target individuals who use substances alone. METHODOLOGY: Grenfell implemented a phone line service initially as a 3-month pilot eventually operationalized to a 24/7 phoneline to determine utilization of the service and test operational feasibility. Metrics such as timing of use, number of unique clients using the service and substances used were measured. RESULTS: The line was provincially utilized. Between February 1st and December 10, 2020, the line was used 64 times. Most calls occurred in the evening, with fentanyl being the most used substance. EMS was dispatched 3 times for overdoses, of which 2 individuals were successfully resuscitated, and one individual's status being unknown. CONCLUSION: The overdose prevention line can be implemented to support individuals who use alone. The service can successfully reduce risk of death from individuals who use alone and could be a valuable tool in addressing the opioid crisis. Further study needs to be conducted to determine its efficacy and safety in supporting clients who 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".