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Record W1941275262 · doi:10.1111/nin.12004

Problematizing health coaching for chronic illness self‐management

2012· review· en· W1941275262 on OpenAlexaff
Lisa Howard, Christine Ceci

Bibliographic record

VenueNursing Inquiry · 2012
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of AlbertaUniversity of Lethbridge
Fundersnot available
KeywordsCoachingSelf-managementHealth careNursingConstraint (computer-aided design)Chronic careHealth coachingGovernment (linguistics)PsychologySociology of health and illnessMedicineChronic diseaseFamily medicinePolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.136
GPT teacher head0.428
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2012
Admission routes1
Has abstractyes

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