Priority setting for Canadian Take-Home Naloxone best practice guideline development: an adapted online Delphi method
Bibliographic record
Abstract
BACKGROUND: Take-Home Naloxone (THN) is a core intervention aimed at addressing the toxic illicit opioid drug supply crisis. Although THN programs are available in all provinces and territories throughout Canada, there are currently no standardized guidelines for THN programs. The Delphi method is a tool for consensus building often used in policy development that allows for engagement of stakeholders. METHODS: We used an adapted anonymous online Delphi method to elicit priorities for a Canadian guideline on THN as a means of facilitating meaningful stakeholder engagement. A guideline development group generated a series of key questions that were then brought to a 15-member voting panel. The voting panel was comprised of people with lived and living experience of substance use, academics specializing in harm reduction, and clinicians and public health professionals from across Canada. Two rounds of voting were undertaken to score questions on importance for inclusion in the guideline. RESULTS: Nine questions that were identified as most important include what equipment should be in THN kits, whether there are important differences between intramuscular and intranasal naloxone administration, how stigma impacts access to distribution programs, how effective THN programs are at saving lives, what distribution models are most effective and equitable, storage considerations for naloxone in a community setting, the role of CPR and rescue breathing in overdose response, client preference of naloxone distribution program type, and what aftercare should be provided for people who respond to overdoses. CONCLUSIONS: The Delphi method is an equitable consensus building process that generated priorities to guide guideline development.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".