Improving ways of working with researchers with lived expertise (of homelessness)
Bibliographic record
Abstract
It has become increasingly apparent that conducting rigorous, relevant and accepted research requires including Researchers with Lived Expertise/Experience (RWLE) in the research process. Including RWLE can create new knowledge rooted in new perspectives, and support research that is more relevant to affected populations. However, the conversation has primarily focused on how involving RWLE can improve research outcomes, so that inequities are being better addressed; there is very little focus on how involvement in research can and should benefit RWLE themselves, nor of how the research process itself can demonstrate a commitment to addressing inequities. In this commentary, we reflect on the experiences of two RWLE of Homelessness, and a Senior Research Associate who all worked together on a recent study. Informed by the challenges we faced and ways we navigated these, here we discuss key issues that must be given more consideration as involving RWLE becomes a necessary part of conducting research. Research teams must consider the issues of pay equity and job security for RLWE who often work on short-term contracts; supporting the professional development of RWLE for their own career advancement; and paying attention to the language we use and how we communicate research findings so they are accessible. There is a unique opportunity for research teams to incorporate a philosophical and practical orientation towards equity during the research process. While research seeks to understand and explain a phenomenon, it must simultaneously seek to address this very phenomenon through how the research is conducted. Our aim is to further the discussion around including RWLE, and to provide tangible suggestions for research organizations and teams. It is widely understood that involving individuals with lived expertise, often called Researchers with Lived Expertise/Experience (RWLE), in research is important. There are clear benefits for the relevance, quality, and acceptance of research findings. Including RWLE can create new knowledge and perspectives, and support research that is more relevant to the populations experiencing issues. However, the conversation has primarily focused on how involvement of RWLE can improve the research outcomes, so that inequities are being better addressed; there is very little focus on how involvement in research should benefit RWLE themselves, nor of the broader need to address inequities through the research process (not just the research outcomes). Research teams must consider the issues of pay equity and job security for RLWE who often work on short-term contracts; supporting the professional development of RWLE for their own career advancement; and paying attention to the language we use and how we communicate research findings so it is accessible. In this commentary, we reflect on the experiences of two RWLE of Homelessness, and a Senior Research Associate who all worked together on a recent study. Through the challenges we faced and ways we navigated these, we discuss key issues that must be given more consideration as involving RWLE is recognized as a necessary part of conducting research.
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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 | 0.338 | 0.348 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.046 | 0.088 |
| Scholarly communication | 0.059 | 0.081 |
| Open science | 0.013 | 0.093 |
| Research integrity | 0.021 | 0.036 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".