Lessons learned from ramping up a Canadian Take Home Naloxone programme during a public health emergency: a mixed-methods study
Bibliographic record
Abstract
OBJECTIVES: This study describes the 2016 expansion of the British Columbia Take Home Naloxone (BCTHN) programme quantitatively and explores the challenges, facilitators and successes during the ramp up from the perspectives of programme stakeholders. DESIGN: Mixed-methods study. SETTING: The BCTHN programme was implemented in 2012 to reduce opioid overdose deaths by providing naloxone kits and overdose recognition and response training in BC, Canada. An increase in the number of overdose deaths in 2016 in BC led to the declaration of a public health emergency and a rapid ramp up of naloxone kit production and distribution. BCTHN distributes naloxone to the five regional health authorities of BC. PARTICIPANTS: Focus groups and key informant interviews were conducted with 18 stakeholders, including BC Centre for Disease Control staff, urban and rural site coordinators, and harm reduction coordinators from the five regional health authorities across BC. PRIMARY AND SECONDARY OUTCOME MEASURES: Take Home Naloxone (THN) programme activity, qualitative themes and lessons learnt were identified. RESULTS: In 2016, BCTHN responded to a 20-fold increase in demand of naloxone kits and added over 300 distribution sites. Weekly numbers of overdose events and overdose deaths were correlated with increases in THN kits ordered the following week, during 2013-2017. Challenges elicited include forecasting demand, operational logistics, financial, manpower and policy constraints. Facilitators included outsourcing kit production, implementing standing orders and policy changes in naloxone scheduling, which allowed for easier hiring of staff, reduced paperwork and expanded client access. CONCLUSION: For THN programmes preparing for potential increases in naloxone demand, we recommend creating an online database, implementing standing orders and developing online training resources for standardised knowledge translation to site staff and clients.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".