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Implementing the British Columbia practice support program in end of life care

2012· article· en· W2042134669 on OpenAlexaffabout
C Clelland, Doris Barwich, D McGregor

Bibliographic record

VenueBMJ Supportive & Palliative Care · 2012
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsVancouver Coastal HealthFraser Health
Fundersnot available
KeywordsPalliative careBest practiceNursingMedicineHealth careChristian ministryEnd-of-life careMedical educationPolitical science

Abstract

fetched live from OpenAlex

The British Columbia (BC) Practice Support Program, supported by the General Practice Services Committee (a joint program of the BC Medical Association, the Ministry of Health), has been an effective strategy to change and influence the care being offered in GP offices throughout BC. This innovative program has engaged 2000 GPs (of ∼3500 caring for 4.5 million people) in practice redesign since 2007 and now is providing a module to improve the identification, assessment and management of patients at the end of life, including redesign of office processes and procedures that need to be implemented to support a new approach. It integrates aspects of the Gold Standards Framework (UK), the BC Chronic disease management framework, the triple aim of the Institute for Health Care Improvement, with the norms and practices of Hospice Palliative Care in Canada. Participants include GP champions as the teachers together with local palliative care providers, home care nurses, specialist physicians and general practitioners and their MOAs. There are 3 learning sessions interspersed with 2 action periods during which physicians are supported to implement changes in practice like a registry; flags to alert the physician to key changes; more collaborative practice and supports for Advance Care Planning. The algorithm and the clinical support tools embedded in the algorithm have been well received and reinforce a best practice approach. The results of the evaluation will be presented. This may enable others tasked with physician education and practice support to learn from a successful, innovative provincial implementation.

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)0.009
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.624

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0020.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.002

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 head0.096
GPT teacher head0.457
Teacher spread0.360 · 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

Citations2
Published2012
Admission routes2
Has abstractyes

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