Creating for and with Patients – How (pre-)clinical scientists transform into agents of patient involvement – or not
Bibliographic record
Abstract
Patient-centeredness through patient involvement (PI) is a cornerstone of the Dutch health system, advocating for inclusive decision-making, and a holistic approach to patient well-being. This idea(l) is enacted in various research projects, such as CIRCULAR – a massive research endeavor focusing on the molecular pathology of, and innovative solutions for atrial fibrillation. However, the question arises, how consortium researchers, who operate in pre-clinical laboratory settings, engage with this concept and the professed “bridging” between the academic research space and the practical patient space. How is this gap to be understood? On what terms is a transformation towards PI possible? And could a liminal lens help to make sense of the bridging? As observed in prior research and preliminary ethnographic fieldwork, pre-clinical researchers struggle with the concept of PI and ways to implement it in their work. Based on personal, behavioural and environmental factors of individual researchers, different experiences and needs co-exist and call for attention if “meaningful PI” is to be realised. Existing tools nowadays only partially address those needs and experiences. Hence, receiving additional support in navigating the research system from PI scholars seems to be still highly important. Taking this line of inquiry beyond the remit of ethnography, I intend to conduct a training/reflection series on PI for consortium members. In the panel, I invite participants to creatively engage in a discussion on the planned work and the question, of whether and how this work could become transformative for (pre-)clinical researchers.
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 | 0.067 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.047 |
| Scholarly communication | 0.029 | 0.032 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".