Reconceptualizing patient engagement within research contexts through a relational approach
Bibliographic record
Abstract
Currently within the Canadian research landscape, inclusion of patients as partners, research ambassadors have become part of the fabric for research funding;"nothing about me without me". My recent personal experiences at pain conferences and from research team meetings as a patient or more precisely, a person living with chronic pain (PLCP), who is also an academic researcher, suggest we need to evolve a philosophy of engagement that serves both the PLCPs as research ambassadors, rather than patent partners. This presentation is intended to open up conversations about the role of patient experience and the interconnections needed to build strong research communities, through a consideration of a whole person care relational model. In order to meaningfully locate and describe the role of the patient within the structure of a scientific research community I turn to Merleau-Ponty who aptly described the two main perspectives from which we research as, “[t]he world and man [human-beings] are accessible through two kinds of investigations, in the first case explanatory [scientific] and in the second case reflective [philosophical]”. Suggesting, that the language and relationships the emerge and nurtured within research communities need a shared understanding derived from a relational approach rather than a business model of efficiency, experts and teams. A relational approach works toward co-creating a sense of belonging and purpose rather than mere inclusion to meet research funding application criteria. The focus of this presentation is to explore how to co-create a relational approach for researchers with people living with chronic pain.
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 | 0.126 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.025 | 0.124 |
| Scholarly communication | 0.044 | 0.037 |
| Open science | 0.008 | 0.052 |
| Research integrity | 0.011 | 0.017 |
| 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".