Guidance on authorship with and acknowledgement of patient partners in patient-oriented research
Bibliographic record
Abstract
The Strategy for Patient-Oriented Research Chronic Pain Network was founded in 2016 and is a patient-oriented research network funded by the Canadian Institutes of Health Research. The Network incorporates patient partners throughout its governance and operations meaning that patient partners may contribute to research projects in ways that warrant scientific authorship as defined by the International Committee of Medical Journal Editors. The Network did a brief informal review of guidance on patient authorship in 2019, but could not find any practical documentation to guide its members on this topic. Note the term patient partner here refers to a patient (or caregiver or other person with lived experience) who is a partner or collaborator on a research team. This guidance does not address patients as participants in a research study. This guidance has been co-written by a group of researchers and patient partners of the Chronic Pain Network in an effort to address this gap. It is intended for both researchers and patient partner audiences. This guidance is meant to facilitate conversations between researchers and patient partners about authorship and/or acknowledgement regarding research projects on which they collaborate. While the overall principles of academic authorship and acknowledgement remain unchanged, nuances for interpreting these principles through the lens of patient engagement or patient-oriented research is provided. Teams that carry out patient-oriented research projects will require different preparation to empower all team members (researchers and patient partners) to discuss authorship and acknowledgement. To facilitate these conversations, we have included an overview of the scientific publishing process, explanation of some common terms, and sets of considerations are provided for both patient partners and researchers in determining the range of team member contribution from acknowledgement to authorship. Conversations about authorship can be difficult, even for established research teams. This guidance, and the resources discussed within it, are provided with the intention of making these conversations easier and more thoughtful.
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 | 0.322 | 0.543 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.023 | 0.023 |
| Insufficient payload (model declined to judge) | 0.020 | 0.029 |
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".