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Record W2096967676 · doi:10.3109/13561820903550671

Situating Primary Health Care within the International Classification of Functioning, Disability and Health: Enabling the Canadian Family Health Team Initiative

2010· article· en· W2096967676 on OpenAlexafffundabout
Sinéad Dufour, S. Deborah Lucy

Bibliographic record

VenueJournal of Interprofessional Care · 2010
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsWestern University
FundersHealth Canada
KeywordsInternational Classification of Functioning, Disability and HealthSituatedNursingHealth carePrimary health careQuality (philosophy)Family healthPsychologyMedicineMedical educationPolitical science

Abstract

fetched live from OpenAlex

Primary health care (PHC) mandates the provision of services delivered by a collaborative team of providers, ultimately to improve quality of care and health status. Considering the challenges related to interprofessional collaboration within novel PHC models, we explored how the World Health Organization's (WHO) International Classification of Functioning, Disability and Health (ICF) could facilitate the enactment of PHC teams. The Canadian Family Health Team (FHT) initiative is used as an example. This paper will explore how the ICF could inform the development of a practice model to enable PHC. Three potential barriers to the envisioned enactment of PHC within the espoused Canadian FHT initiative are identified through a critical gaps analysis; lack of (i) philosophical grounding, (ii) developmental and operational directives, and (iii) evaluation methods. An ICF-informed practice model is proposed to overcome these potential barriers. It is argued that the proposed ICF-informed practice model has international implications as a unifying conceptual framework ideally situated to facilitate the provision of comprehensive evidence-based person-centered care by interprofessional collaborative teams within diverse PHC models.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.057
GPT teacher head0.432
Teacher spread0.375 · 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

Citations25
Published2010
Admission routes3
Has abstractyes

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