Engaging people with lived experience in the grant review process
Bibliographic record
Abstract
People with lived experience are individuals who have first-hand experience of the medical condition(s) being considered. The value of including the viewpoints of people with lived experience in health policy, health care, and health care and systems research has been recognized at many levels, including by funding agencies. However, there is little guidance or established best practices on how to include non-academic reviewers in the grant review process. Here we describe our approach to the inclusion of people with lived experience in every stage of the grant review process. After a budget was created for a specific call, a steering committee was created. This group included researchers, people with lived experience, and health systems administrators. This group developed and issued the call. After receiving proposals, stage one was scientific review by researchers. Grants were ranked by this score and a short list then reviewed by people with lived experience as stage two. Finally, for stage three, the Steering Committee convened and achieved consensus based on information drawn from stages one and two. Our approach to engage people with lived experience in the grant review process was positively reviewed by everyone involved, as it allowed for patient perspectives to be truly integrated. However, it does lengthen the review process. The proposed model offers further practical insight into including people with lived experience in the review process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.507 | 0.532 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.007 | 0.055 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".