Problematizing health coaching for chronic illness self‐management
Bibliographic record
Abstract
To address the growing costs associated with chronic illness care, many countries, both developed and developing, identify increased patient self-management or self-care as a focus of healthcare reform. Health coaching, an implementation strategy to support the shift to self-management, encourages patients to make lifestyle changes to improve the management of chronic illness. This practice differs from traditional models of health education because of the interactional dynamics between nurse and patient, and an orientation to care that ostensibly centres and empowers patients. The theoretical underpinnings of coaching reflect these differences, however in its application, the practices arranged around health coaching for chronic illness self-management reveal the social regulation and professional management of everyday life. This becomes especially problematic in contexts defined by economic constraint and government withdrawal from activities related to the 'care' of citizens. In this paper, we trace the development of health coaching as part of nursing practice and consider the implications of this practice as an emerging element of chronic illness self-management. Our purpose is to highlight health coaching as an approach intended to support patients with chronic illness and at the same time, problematize the tensions contained in (and by) this practice.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".