“Inclusivity requires an active effort”: building an inclusive and diverse space when engaging people with lived and living experience and caregivers in mental health and substance use health research
Bibliographic record
Abstract
BACKGROUND: Engaging people with lived/living experience and caregivers (PLLEX-C) in mental health and substance use health research has many advantages for scientists, research staff, and PLLEX-C. However, research teams must be cognizant of the wide spectrum of human diversity. As such, engagement settings that are inclusive and reflect the diversity of the populations being served are essential to meaningful and impactful research. OBJECTIVE: The aim of this qualitative descriptive study was to understand the perspectives of PLLEX-C on how to build inclusive and diverse research spaces when engaging PLLEX-C in mental health and substance use health research. METHODS: We recruited 20 PLLEX-C with experience engaging in mental health and substance use health research to participate in one of five focus group discussions. The focus groups were audio recorded and transcribed, with codebook thematic analysis conducted using a deductive and inductive approach. This study team included a Lived and Living Experience and Caregiver Working Group throughout all phases of the research project. RESULTS: Four themes were identified across the five focus group discussions: 1) Acknowledge that diversity is inclusive of different factors and this needs to be reflected in the recruitment process to improve the research. 2) Remove barriers of entry into the research space. 3) Ensure that the scientists and staff are trained and skilled in inclusive and diverse engagement. 4) Build a safe and equitable engagement space. Within these themes, subthemes were also identified and described with illustrative quotes. CONCLUSIONS: Identifying ways to ensure research engagement settings are inclusive and diverse for all those involved requires an intentional and active effort at all stages of the research process. This includes not only employing various recruitment strategies to identify more diverse PLLEX-C, but also continuous, ongoing training for researchers to ensure engagement is culturally sensitive, anti-discriminatory, and bias-free. Prioritizing research teams that are inclusive and diverse can foster an engagement experience that is more meaningful, authentic, and empowering.
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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 | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".