Factors associated with willingness to wear an electronic overdose detection device
Bibliographic record
Abstract
BACKGROUND: North America is in the midst of an opioid overdose epidemic. Although take-home naloxone and other measures have been an effective strategy to reduce overdoses, many events are unwitnessed and mortality remains high amongst those using drugs alone. While wearable devices that can detect and alert others of an overdose are being developed, willingness of people who use drugs to wear such a device has not been described. METHODS: Drug using persons enrolled in a community-recruited cohort in Vancouver, Canada, were asked whether or not they would be willing to wear a device against their skin that would alert others in the event of an overdose. Logistic regression was used to identify factors independently associated with willingness to wear such a device. RESULTS: Among the 1061 participants surveyed between December 2017 and May 2018, 576 (54.3%) were willing to wear an overdose detection device. Factors independently associated with willingness included ever having overdosed (adjusted odds ratio [AOR] = 1.39, 95% confidence interval [CI] 1.06-1.83), current methadone treatment (AOR = 1.86, 95% CI 1.45-2.40), female gender AOR = 1.41, 95% CI 1.09-1.84) and a history of chronic pain (AOR = 1.53, 95% CI 1.19-1.96). Whereas homelessness (AOR = 0.67, 95% CI 0.50-0.91) was negatively associated with willingness. CONCLUSIONS: A high level of willingness to wear an overdose detection device was observed in this setting and a range of factors associated with overdose were positively associated with willingness. Since some factors, such as homelessness may be a barrier, further research is needed to investigate explanations for unwillingness and to evaluate real world acceptability of a wearable overdose detection devices as this technology becomes available.
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 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 | 0.009 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".