Bibliographic record
Abstract
Background/Objectives Collaborative research with community members has been identified as “one of the best ways to support rapid application of research evidence” by focusing research to the needs of users. As funders and researchers rush to bridge the gap in healthcare delivery, there arises a need to build on integrated knowledge translation (IKT) best practices to support equitable PLEX compensation. This research will help determine the monetary value of PLEX when participating in research. Describe the methods used to quantify lived experience compensation rates across the healthcare spectrum; including BCCDC, Diabetes Action Canada, CIHR Genetics, National Health Council, Chronic Pain Network etc. Determine an equitable PLEX compensation rate based on current research; Share research to help guide equitable compensation rates. Methodology An environmental scan of healthcare funders, institutions and organizations patient partners /lived experience compensation rates in Canada, United States, and select countries from 2010-2023 provided a baseline data set. A systematic scan of published literature and grey literature of this nascent field, PLEX compensation rates was synthesized from this analysis and aligned with the SPOR guidelines. A third party conducted a check of the data abstraction to ensure high data quality. Results Early results indicate a wide range of patient compensation rates from $40-200 a session with limited information on SCI-specific compensation. Conclusion This research provides a rationale for fair and equitable compensation of PLEX, with a compensation matrix based on the level of engagement.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.818 | 0.345 |
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".