Investigating uses of peer-operated Virtual Overdose Monitoring Services (VOMS) beyond overdose response: a qualitative study
Bibliographic record
Abstract
Background: Virtual overdose monitoring services (VOMS) are novel technologies that allow remote monitoring of individuals while they use substances (especially those who use alone) electronically.Objectives: The authors explored key partner perspectives regarding services offered by VOMS beyond overdose response with the aim of understanding the breadth and perception of the services amongst those that use these services and are impacted by them.Methods: Forty-seven participants from six key partner groups [peers who had used VOMS (25%), peers who had not used VOMS (17%), family members of peers (11%), health professionals (21%), harm reduction sector employees (15%), and VOMS operators (15%)] underwent 20-to-60-minute semi-structured telephone interviews. Of peer and family groups, thirteen participants identified as female, eleven as male and one as non-binary, gender data was not recorded for other key partner groups. Interview guides were developed and interviews were conducted until saturation was reached across all participants. Themes and subthemes were identified and member checked with partner groups.Results: Participants indicated that uses of VOMS beyond overdose monitoring included: (1) providing mental health support and community referral; (2) methamphetamine agitation de-escalation; (3) advice on self-care and harm reduction; and (4) a sense of community and peer support. Respondents were divided on how VOMS might affect emergency services (5).Conclusions: VOMS are currently being used for purposes beyond drug poisoning prevention, including community methamphetamine psychosis de-escalation, mental health support, and community peer support. VOMS are capable of delivering a broad suite of harm reduction services and referring clients to recovery-oriented services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".