The voices of lived experience: reflections from citizen team members in a long-term care research program
Bibliographic record
Abstract
BACKGROUND: The Translating Research in Elder Care (TREC) program is a partnered health services research team that aims to improve the quality of care and quality of life for residents and quality of worklife for staff in nursing homes. The TREC team undertook several activities to enhance the collaboration between the academic researchers and us, the citizen members. Known as VOICES (Voice Of (potential) Incoming residents, Caregivers Educating uS) we aim to share our experience working with a large research team. METHODS: We reflect on the findings reported in the paper by Chamberlain et al. (2021). They described the findings from two surveys (May 2018, July 2019) that were completed by TREC team members (researchers, trainees, staff, decision-makers, citizens). The survey questions asked about the respondents' experience with citizen engagement, their perceptions of the benefits and challenges of citizen engagement, and their unmet needs for training. RESULTS: The paper reported on the survey findings from all the survey respondents (research team, decision-makers, citizens), but much of the results focused on the researcher perspective. They reported that respondents believed that citizen engagement was a benefit to their research but noted many challenges. While we appreciate the researchers' positive perceptions of citizen engagement, much work remains to fully integrate us into all stages of the research. We offer our reflections and suggestions for how to work with citizen members and identify areas for more training and support. CONCLUSIONS: Despite the increased interest in citizen engagement, we feel there is a lack of understanding and support to truly integrate non-academic team members on research teams. We hope the discussion in this commentary identifies specific areas that need to be addressed to support the continued engagement of citizens and show how the lived experience can bring value to research teams.
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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 | 0.061 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.046 | 0.040 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.007 | 0.034 |
| Research integrity | 0.010 | 0.025 |
| 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, 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".