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Record W2120009717 · doi:10.1177/1049732310372376

Giving Patients Responsibility or Fostering Mutual Response-Ability: Family Physicians’ Constructions of Effective Chronic Illness Management

2010· article· en· W2120009717 on OpenAlexaffabout
Patricia Thille, Grant Russell

Bibliographic record

VenueQualitative Health Research · 2010
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPaternalismVisionConceptualizationGeneral partnershipMedicineChronic careResistance (ecology)PsychologyNursingFamily medicineChronic diseaseSociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Current visions of family medicine and models of chronic illness management integrate evidence-based medicine with collaborative, patient-centered care, despite critiques that these constructs conflict with each other. With this potential conflict in mind, we applied a critical discursive psychology methodology to present discursive patterns articulated by 13 family physicians in Ontario, Canada, regarding care of patients living with multiple chronic illnesses. Physicians constructed competing versions of the terms "effective chronic illness management" and "patient involvement." One construction integrated individual responsibility for health with primacy of "evidence," resulting in a conceptualization consistent with paternalistic care. The second constructed effective care as involving active partnership of physician and patient, implying a need to foster the ability of both practitioners and patients to respond to complex challenges as they arose. The former pattern is inconsistent with visions of family medicine and chronic illness management, whereas the latter embodies it.

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)0.040
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0400.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
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 head0.509
GPT teacher head0.614
Teacher spread0.105 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations27
Published2010
Admission routes2
Has abstractyes

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