Bridging the gap: Engaging people with lived experience and health care providers on research review panels
Bibliographic record
Abstract
Abstract Background The Alzheimer Society of Canada’s prevailing theme is “nothing for us, without us”. This value is embedded in all aspects of their work. Since 2015, people with lived experience of dementia have been engaged in the organization’s annual grant competition, sharing their perspectives of grant applications on peer review panels. In 2019, the Alzheimer Society of Canada introduced a relevancy review panel to supplement the peer review process. This interdisciplinary panel included organizational staff with direct contact with people with lived experience as well as citizen reviewers. Collectively, they reviewed and scored research proposals for clarity and relevance to their experience, contributing to recommendations about which proposals should receive funding. Method In 2019, the Alzheimer Society of Canada recruited seven people with lived experience of dementia and five staff from across the country to become more deeply engaged in decision‐making for the society’s annual grant‐awarding competition. Participants attended an inaugural training workshop to learn more about the organization’s research program and its funding priorities while helping to co‐develop the training to address further need. The workshop was evaluated and feedback used to inform the planning and delivery of future engagement sessions. Participants were subsequently allocated to either the Peer Review Panel or Relevancy Review Panel. Their involvement on these panels will be evaluated in Spring 2020 and results used to inform future panel participation. Result All the participants agreed that the training workshop achieved its objectives. The workshop provided an opportunity for people with lived experience and staff to engage directly with each other while co‐designing the scoring criteria and guidelines of the panel. It also created a new opportunity to meaningfully engage people with lived experience in the research process while balancing the expertise required to assess proposals. Participants’ involvement on the research panels will be evaluated in Spring 2020. Conclusion People with lived experience of dementia are important partners in the research process. Their practical involvement in scoring research proposals for importance and relevance will ensure that research more closely meets their needs while developing a new role for their meaningful engagement.
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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 | 0.351 | 0.378 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.037 | 0.025 |
| Scholarly communication | 0.031 | 0.032 |
| Open science | 0.011 | 0.073 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".