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Record W2727881307 · doi:10.5334/ijic.2483

Are Patient and Carer Experiences Mirrored in the Practice Reviews of Self-management Support (Prisms) Provider Taxonomy?

2017· article· en· W2727881307 on OpenAlexaff
Nicolette Sheridan, Timothy Kenealy, Kerry Kuluski, Ann McKillop, John Parsons, Cecilia Wong-Cornall

Bibliographic record

VenueInternational Journal of Integrated Care · 2017
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTaxonomy (biology)NursingMedicineSelf-managementMedical educationPsychologyComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: Patient self-management support is central to care for long term conditions and for integrated care. Patients and their carers are the final arbiter of whether support for self-management has been effective. A new taxonomy lists 14 categories of provider activities that support patient self-management (Practical Reviews in Self-Management Support, PRISMS). We asked whether we could recognise these provider activities in narratives from patients and carers. We sought to extend the theoretical framework of the taxonomy to include the view from patient and carers. METHODS: We interviewed 28 patients and family carers in a case study of primary health care in New Zealand in 2015 to determine which components of the taxonomy were visible. We drew on interviews with clinicians and organisation persons to explain case study context. RESULTS: We found, within patient and carer data, evidence of all 14 components of provider self-management support. The overarching dimensions of the taxonomy helped reveal an intensity and consistency of provider behaviour that was not apparent considering the individual components. CONCLUSIONS: Patient and carer data mapped to provider activities. The taxonomy was not explicit on provider relationships and engagement with, or separate support needs of, patients and carers.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.029
GPT teacher head0.327
Teacher spread0.298 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2017
Admission routes1
Has abstractyes

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