Walk with me - What it means to care: Activating compassionate quality care for people who use substances against the backdrop of Island Health's harm reduction-substance use policy
Bibliographic record
Abstract
Since labelled a provincial emergency in 2016, the toxic drug poisoning crisis in B.C. has claimed over 14,000 lives. Government, health and community service providers alike have struggled to find solutions to the crisis and have developed numerous interventions aimed to reduce deaths, harm and stigma. Despite these efforts, toxic drug deaths continue to climb, with 2023 recording the most fatalities ever and no sign of slowing in 2024. “Walk With Me” is a research and community action project developed in small B.C. communities, beginning in Comox Valley and Campbell River, B.C. The project began in 2019 as a partnership between Comox Valley Art Gallery, Thompson Rivers University and AVI Health & Community Services, aiming to develop humanistic and systems-based solutions to the toxic drug poisoning crisis. Beginning in 2021, the Walk With Me team was invited to work with Island Health to engage staff in facilities across Vancouver Island in a multi-tiered research initiative. The research invited staff from these facilities join “Story Walks”—a series of guided listening journeys foregrounding local first-hand testimony of the crisis. Following the walks, staff were invited to sit in-circle, and to share insights and respond to the questions: “What is Island Health doing well to support those at the heart of the toxic drug poisoning crisis?” and “How can Island Health better support people at the heart of the toxic drug poisoning crisis?” In collecting and analyzing staff insights, the project aims to illuminate ways forward for Island Health towards progressive institutional change.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".