Preference for hotline versus mobile application/countdown-based mobile overdose response services: a qualitative study
Bibliographic record
Abstract
BACKGROUND: In response to the exacerbated rates of morbidity and mortality associated with the overlapping overdose and COVID-19 epidemics, novel strategies have been developed, implemented, operationalized and scaled to reduce the harms resulting from this crisis. Since the emergence of mobile overdose response services (MORS), two strategies have aimed to help reduce the mortality associated with acute overdose including staffed hotline-based services and unstaffed timer-based services. In this article, we aim to gather the perspectives of various key interest groups on these technologies to determine which might best support service users. METHODS: Forty-seven participants from various interested groups including people who use substances who have and have not used MORS, healthcare workers, family members, harm reduction employees and MORS operators participated in semi-structured interviews. Transcripts were coded and analyzed using a thematic analysis approach. RESULTS: Four major themes emerged regarding participant perspectives on the differences between services, namely differences in connection, perceived safety, privacy and accessibility, alongside features that are recommended for MORS in the future. CONCLUSIONS: Overall, participants noted that individuals who use substances vary in their desire for connection during a substance use session offered by hotline and timer-based service modalities. Participants perceived hotline-based approaches to be more reliable and thus potentially safer than their timer-based counterparts but noted that access to technology is a limitation of both approaches.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".