The engagement of people with lived experiences in substance use research
Bibliographic record
Abstract
Background: People with lived experiences (PWLE) are underrepresented in research engagement, however their involvement can significantly boost the relevance and impact of research. Questions concerning the credibility and trustworthiness of PWLE researchers by traditional and positivist researchers have been identified. Having been associated with substance use of questionable legality and related substance use activities, PWLE researchers face stigma and are deemed to lack the trustworthiness that serious research entails. Current literature on PWLE found a dearth of knowledge on the definitions and conceptualizations of PWLE in research, which this paper attempts to partially address. Methods: Issues surrounding the trustworthiness of PWLE in substance use research were investigated, along with accounts of involving PWLE at different phases of the research process. Findings: People with lived experiences have been undervalued as researchers compared to other positivist counterparts despite advocating against marginalization and oppressive practices. They offer in-depth, meaningful contributions to research involving phenomena that they have experienced and were found to provide insights that other non-PWLE researchers overlooked. Moreover, engaging PWLE in research is not only beneficial for research processes and outcomes but is also empowering for PWLE themselves. Conclusion: A guide to maintaining trustworthiness and a description of PWLE contributions to research processes are provided.
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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 | 0.031 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".