Putting Indigenous Harm Reduction to Work: Developing and Evaluating “Not Just Naloxone”
Bibliographic record
Abstract
First Nations people and communities have long been championing the provision of holistic, self-determining, culturally safe, and responsive health care. In April 2016, a catastrophic rise in illicit drug overdose deaths in the province of British Columbia (BC), Canada, led to the declaration of a public health emergency. Due to the compounding historical and ongoing impacts of colonialism, including trauma and inequitable access to health services, First Nations people in BC are disproportionately impacted by this crisis. In response, the First Nations Health Authority created Not Just Naloxone (NJN), a train-the-trainer workshop designed to build Indigenous harm reduction knowledge and skills within First Nations communities. This article describes the NJN program and presents the results of a follow-up evaluation of 37 participants from six NJN workshops held between December 2017 and October 2018. Core strengths of the training included an Indigenized approach and the opportunity to build networks of support. Respondents reported increased knowledge and confidence presenting about harm reduction and feeling more prepared to respond to overdoses. Areas for improvement included maintaining up-to-date training materials and navigating emotional triggers for participants. Trainees went on to train over 2,400 community members in naloxone and Indigenous harm reduction, and reported that communities’ awareness and attitudes around harm reduction began to change. Challenges providing community trainings included buy-in from local leadership and persistent abstinence-based beliefs. This evaluation demonstrates the impact of holistic, culturally safe harm reduction training and the need for a connected community of Indigenous harm reduction champions.
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 | 0.058 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".