Factors associated with willingness to wear an electronic overdose detection device
Why this work is in the frame
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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.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it