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Record W3213807785 · doi:10.35680/2372-0247.1562

The tensions between healthcare providers and patient and family advisory committees (PFACs): A comparative health system analysis between England and Ontario

2021· article· en· W3213807785 on OpenAlexaffabout
Umair Majid

Bibliographic record

VenuePatient Experience Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLegislatureContext (archaeology)Health careFamily medicineAdvisory committeeMedicineHealthcare systemPublic administrationPolitical scienceNursingPublic relationsGeographyLaw

Abstract

fetched live from OpenAlex

There has been a proliferation of patient engagement (PE) in healthcare activities. However, the concept of “engagement” has existed for decades; the first Patient and Family Advisory Committees (PFACs) in North America were formed in the 1970s. These committees are an important mechanism for involving patients and family and have proliferated across the healthcare sector. However, it is unclear how or why PFACs became the predominant mechanism for PE. The objective of this comparative analysis is to review the historical context and legislative imperatives that have contributed to the proliferation of PFACs in Ontario, Canada and England, United Kingdom. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.944

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.010
Science and technology studies0.0120.007
Scholarly communication0.0060.003
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.111
GPT teacher head0.371
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes2
Has abstractyes

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