Bibliographic record
Abstract
The opioid crisis in Alberta is a public health crisis. In 2016, more people died from an opioid poisoning than from motor vehicle crashes. Naloxone is an opioid antagonist which means that it can reverse an opioid overdose for a period of 30–60 minutes, at which point, the overdose may return. In December 2015, the Take Home Naloxone (THN) Program was rolled out in response to the opioid crisis. Under the renamed the Community Based Naloxone Kit Program (CBNP), naloxone kits are now available free of cost at many pharmacies and community clinics around Alberta. The wide availability has led to a new challenge—that the kits may be used by people who have received little to no training.Some may encounter the kit instructions for the first time when there is an emergency in which they need to administer an injection urgently to someone who has passed out. Studies have found that most overdoses occur in the presence of another person—this provides an opportunity for someone to intervene. People often die from witnessed opioid poisonings because other people do not know what to do to help. A pilot study conducted through community partnerships involved 30 participants in two different urban centres (Edmonton and Calgary) who self identified as either experienced in substance use or friends/family of people with lived experience has revealed some interesting findings.Qualitative observations and data collected in the initial pilot work show that end users are experiencing unique challenges in accessing opioid education and have challenges using instructions on how to administer naloxone in an overdose setting. User testing and observation of user behavior has great potential to support educational material for opioid awareness.Human-centred design approaches that gather information with and about people using antidote kits are urgently needed in order to mitigate risk and ensure successful administration of first aid and naloxone in an emergency.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".