Co-constructing experiential knowledge in health: The contribution of people living with Parkinson to the co-design approach
Bibliographic record
Abstract
Background: The use of collaborative approaches in the design of digital health technologies could help researchers to better understand the patient perspective. Starting from a 2019 Canadian case study focused on co-design and Parkinson’s disease, this paper discusses the potential of using narrative interviews to capture the patient experience. Aim: The objectives of this study are to examine the process of co-construction of ‘experiential knowledge’ through the interaction during a narrative interview and stress the significance of this method in relation to a co-design approach. Methods: A qualitative analysis of transcripts from 19 narrative interviews conducted in 2019 with people living with PD and their caregivers was performed. Results: Materialized in embedded, embodied, and emergent knowledge, findings reveal the potential of narrative interviews to provide insight to how experiential knowledge of people living with PD is constituted. Discussion: In addition to generate a learning environment, the analysis indicates that narrative interviews help to make visible experiential knowledge through the interaction processes between patients, caregivers, and researchers. Conclusion: This suggests that narrative interviews permit a more patient-centered design of digital health technologies, as they collect the psychological, social, and medical factors that influence the experience of these individuals.
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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 | 0.049 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".