Understanding Patient Perspectives of Self-Care in the Management of Chronic Disease
Bibliographic record
Abstract
This workshop will provide participants with an understanding of how appreciative inquiry based methods can help us learn from patient experience, develop self-care plans and identify the roles of various support groups within those plans, and develop a framework for chronic disease management which is based on lessons learnt from patient experience.Understanding patient experience with and perspectives on what supports on-going self care to manage chronic disease is critical to a whole person approach to chronic disease care. While the individual’s experience of chronic disease self-management may vary from individual to individual, our recent work demonstrates that appreciative enquiry is an effective tool for understanding the key support structures for effective management of self-care. This work also points to appreciative inquiry as an effective tool for patients and care-givers in developing approaches to individual self-care plans.After a brief presentation of our recent findings, workshop participants will have an opportunity to use a modified appreciative inquiry approach to consider aspects of chronic disease care and particularly to discuss those factors that support chronic disease management. Appreciative Inquiry, as a positive form of narrative inquiry will elicit stories that make these supportive factors more concretely visible and comprehensible. Workshop participants will also be provided with a brief overview of thematic analysis as a way of identifying common themes in the appreciative inquiry data and developing a model of self-care support.
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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 | 0.031 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.012 |
| 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".