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Record W4393861757 · doi:10.1177/08404704241239864

Growing a provincial patient and family engagement network to optimize kidney care

2024· article· en· W4393861757 on OpenAlexafffund
Helen Chiu, Brenda Ken San Lee, Laura Lee Bennett, John Spensley, William Walker Dear, Winphia Koo, S Saunders, Gloria Freeborn

Bibliographic record

VenueHealthcare Management Forum · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsProvidence Health Care
FundersProvincial Health Services AuthorityBC Renal AgencyKidney Foundation of Canada
KeywordsOperationalizationOutreachCommunity engagementPublic relationsHealth carePatient careResource (disambiguation)MedicineNursingBusinessPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Patient and family engagement is crucial for a responsive health system and improves patient outcomes. However, few practical resources for purposeful engagement are available to health leaders. Over the past five years, BC Renal, the provincial kidney care network in British Columbia, developed, operationalized, and implemented a framework to enable meaningful patient and family engagement. An advisory committee, comprising patient partners and representatives from health authorities and the community, directs the outreach, resource development, and evaluation of patient and family engagement at BC Renal. Here, we describe how our network-wide patient engagement strategy was developed and expanded upon, and the progress so far. A 2022 survey reports that 95% were satisfied with the engagement opportunities, and narrative feedback suggests network members continue to adopt practical ways to collaborate more effectively. Health leaders, patient partners, and others continue to align operational and strategic activities to advance culture change in kidney care provincially.

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)0.010
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.719

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.002
Scholarly communication0.0050.002
Open science0.0020.013
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0150.002

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 head0.088
GPT teacher head0.379
Teacher spread0.292 · 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 designObservational
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

Citations1
Published2024
Admission routes2
Has abstractyes

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