Reflections on patient engagement by patient partners: how it can go wrong
Bibliographic record
Abstract
As six patient partners in Canada, we aim to contribute to learning and to provide an opportunity to reflect on patient engagement (PE) in research and healthcare environments. Patient engagement refers to "meaningful and active collaboration in governance, priority setting, conducting research and knowledge translation" with patient partners as members of teams, rather than participants in research or clinical care. While much has been written about the benefits of patient engagement, it is important to accurately document and share what we term 'patient engagement gone wrong.' These examples have been anonymized and presented as four statements: patient partners as a check mark, unconscious bias towards patient partners, lack of support to fully include patient partners, and lack of recognizing the vulnerability of patient partners. The examples provided are intended to demonstrate that patient engagement gone wrong is more common than discussed openly, and to simply bring this to light. This article is not intending to lay blame, rather to evolve and improve patient engagement initiatives. We ask those who interact with patient partners to reflect so we can all work towards improving patient engagement. Lean into the discomfort with these conversations as that is the only way to change these all too recognizable examples, and which will lead to better project outcomes and experiences for all team members.
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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.045 | 0.014 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.044 | 0.074 |
| Insufficient payload (model declined to judge) | 0.005 | 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".