Reflections on conducting Peer-Led Qualitative Research in British Columbia, Canada during COVID-19 Pandemic
Bibliographic record
Abstract
Abstract In the province of British Columbia, illicit drug toxicity (overdose) deaths have increased during the Coronavirus Disease 2019 (COVID-19) pandemic. Prior evidence suggests that engagement of people with lived and living experience (PWLLE) of substance use, often referred to as peers, in research and policy development is essential to ensure the development of comprehensive and relevant harm reduction interventions addressing the requirements of the PWLLE. Public health measures introduced due to COVID-19 have intensified barriers in engaging PWLLE in research settings. This article presents the challenges encountered in conducting peer-led research in BC and the ways in which these challenges were addressed in the context of a province-wide research project initiated by the British Columbia Centre for Disease Control.
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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 | 0.110 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.079 | 0.047 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".