A Reconceptualization of the Negative Self-Stereotyping of the Patient-Partner to the Introduction of the Patient Perspective Consultant
Bibliographic record
Abstract
The label of "patient-partner" is widely used when referring to a person living with a specific health condition that participates in research teams or consults on clinical practice guidelines. However, being a patient-partner says nothing about one's potential role outside a biomedical context. Labeling a person as such can be detrimental to their perception of themselves. The intention of this paper is to provide a philosophical conceptual framework to understand the complexities and consequences of labeling people as patients outside of direct healthcare. A philosophical hermeneutic approach was used to explore how labeling and self-stereotyping can affect the patient-partner, leading to the possible erosion of their personhood. The authors suggest that research teams instead employ the more accurate and dignified term, "patient perspective consultant." Accurate titles allow team members to relate to each other, leaving room for everyone to contribute meaningfully. The shift from patient-partner to patient perspective consultant does not change the nature of the role. It clarifies the context through increased accuracy, and adds dignity and purpose.
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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.081 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".