Researching With Lived Experience: A Shared Critical Reflection Between Co-Researchers
Bibliographic record
Abstract
This paper draws together critical learnings from diverse qualitative health research projects in Australia that sought to shift power and focus on the strengths and expertise of people with lived experience who are involved as co-researchers. These projects have included exploring and challenging identities, understanding experiences in treatment programs, critiquing and designing/re-designing services, and sharing experiences with the wider community in novel and innovative ways. Lived experiences included alcohol and other drug dependency, mental health, domestic, family or sexual violence, and living with HIV. This paper provides important learnings and actions about partnering with co-researchers with lived experience. In this paper we draw on a process of reflective discussions that occurred over six months with fortnightly online meetings between co-researchers, including co-authors with lived experience external to academia and university-based researchers, some of whom also have lived-experience that intersects with their research. From this, we distilled key learnings across seven themes: (1) the ethics of ethics, which highlights a need for constant reflection on the ethical issues in co-research; (2) recruiting co-researchers, which focuses on ensuring and integrating a diversity of voices; (3) creating safety for all, which must be a priority of engagement and support self-determination; (4) supporting different ways of partnering, which emphasises the need for diverse roles and ways to contribute on research teams; (5) capacity building and training, which requires ongoing evaluation of needs and tailored responses; (6) positioning, which highlights the need to transition from the idea of vulnerability to a strengths-based perspective of lived experience; and (7) power plays, reflecting the need to disrupt the dynamics and established hierarchies of privileging certain forms of knowledge and expertise. The paper includes recommendations for action against these seven themes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.151 | 0.207 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.032 | 0.080 |
| Scholarly communication | 0.034 | 0.028 |
| Open science | 0.009 | 0.045 |
| Research integrity | 0.010 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".