Engagement of persons with lived experience in research that uses linked administrative health data - the BC Provincial Overdose Cohort.
Bibliographic record
Abstract
Illicit drug overdose is a significant public health challenge in British Columbia (BC) that has been worsened by the COVID-19 pandemic. In 2021, 2224 persons died of overdose in BC– more than any year on record. The vast majority of overdose deaths are attributed to consumption of illicit substances and poisoning due to fentanyl. Since the 2016 emergency declaration, efforts have been made to create new data infrastructure that allows for comprehensive ascertainment of non-fatal and fatal overdose. The BC Provincial Overdose Cohort (BC-ODC) is a unique cohort that was created under public health order that includes all identified cases of illicit drug overdose in BC from January 1 2015-December 31, 2020 that is updated annually. With its unique data extracts, shared data governance structure and novel mandate, a critical component of stewarding the BC-ODC is engagement of persons with lived experience of substance use, overdose and/or incarceration. Two initiatives were launched in 2019 and 2022 to work with persons with lived experience to prioritize research that uses the BC-ODC data. Engagement of persons with lived experience of incarceration started in 2022 to inform projects that examined topics related to decriminalization, criminal legal system involvement (charges and convictions) and incarceration. This presentation will describe these initiatives to show the importance of peer involvement in research that uses linked administrative health data particularly when designing, interpreting and disseminating findings to reduce stigma towards persons who use substances.
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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 | 0.034 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".