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Record W2507137352 · doi:10.1108/ijhg-01-2016-0003

The patient's voice in health and social care professional education

2016· article· en· W2507137352 on OpenAlexaff
Angela Towle, Christine Farrell, Martha E. Gaines, William Godolphin, Gabrielle John, Cathy Kline, Beth A. Lown, Penny Morris, Jools Symons, Jill Thistlethwaite

Bibliographic record

VenueInternational Journal of Health Governance · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsLearning PartnershipUniversity of British Columbia
Fundersnot available
KeywordsStatement (logic)Multidisciplinary approachPublic relationsHealth careOriginalityMission statementAction (physics)MedicineMedical educationValue (mathematics)Political sciencePsychologyNursingSociologyQualitative researchComputer scienceSocial science

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to present a statement about the involvement of patients in the education of health and social care professionals developed at an international conference in November 2015. It aims to describe the current state and identify action items for the next five years. Design/methodology/approach – The paper describes how patient involvement in education has developed as a logical consequence of patient and public participation in health care and health research. It summarizes the current state of patient involvement across the continuum of education and training, including the benefits and barriers. It describes how the conference statement was developed and the outcome. Findings – The conference statement identifies nine priorities for action in the areas of policy, recognition and support, innovation, research and evaluation, and dissemination and knowledge exchange. Originality/value – The conference statement represents the first time that an international and multidisciplinary group has worked together to assemble in a single document specific priorities for action to embed the patient’s voice in health professional education.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.074
GPT teacher head0.452
Teacher spread0.378 · 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 designNot applicable
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

Citations93
Published2016
Admission routes1
Has abstractyes

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