Patients, persons or partners? Involving those with chronic disease in their care
Bibliographic record
Abstract
Self-care management is essential for effective chronic disease management. Yet prevailing approaches of healthcare practitioners often undermine the efforts of those who require on-going medical attention for chronic conditions, emphasizing their status as patients, failing to consider their larger life experience as people, and most importantly, failing to consider them as people with the potential to be partners in their care. This article explores two approaches for professional-patient interaction in chronic disease management, namely, patient-centred care and empowering partnering, illuminating how professionals might better interact with chronically ill individuals who seek their care. The opportunities, challenges, theory and research evidence associated with each approach are explored. The advantages of moving beyond patient-centred care to the empowering partnering approach are elaborated. For people with chronic disease, having the opportunity to engage in the social construction of their own health as a resource for everyday living, the opportunity to experience interdependence rather than dependence/independence throughout on-going healthcare, and the opportunity to optimize their potential for self-care management of chronic disease are important justifications for being involved in an empowering partnering approach to their chronic disease management.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".