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Advance-care planning quality improvement plan: A Cancer Care Ontario toolkit to support primary care teams to implement advance care plans in practice.

2014· article· en· W2590368828 on OpenAlexaffabout
Jeff Myers, Suzanne Strasberg, Kathi Carroll, Zabin Dhanji, Ingrid Harle, Sara Urowitz, Tara Walton, Victoria Zwicker

Bibliographic record

VenueJournal of Clinical Oncology · 2014
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsKingston General HospitalHealth Sciences CentreCancer Care OntarioSunnybrook Health Science Centre
Fundersnot available
KeywordsAdvance care planningMedicineQuality managementQuality (philosophy)Health careNursingProcess managementPlan (archaeology)PDCABest practiceProcess (computing)Christian ministryPalliative careOperations managementBusinessComputer scienceEngineering

Abstract

fetched live from OpenAlex

76 Background: In Ontario, the Ministry of Health and Long Term Care’s (MOHLTC) uses Quality Improvement Plans (QIPs) to drive system improvement aimed at providing high value, high quality care for all. To support the introduction of QIPs into the primary care sector, Cancer Care Ontario has developed an Advance Care Planning (ACP) toolkit for practices that include ACP as part of their annual QIP. ACP is an ongoing and dynamic process that involves a capable individual reflecting on their current values and beliefs for their health care, communicating their personal wishes for future health care and identifying an individual who will make decisions on their behalf in the event that they are unable to provide informed consent. The process is iterative and wishes may change over time with changes in health status. Methods: The ACP QIP was developed based on the Plan, Do, Study, Act cycle of continuous quality improvement. The ACP QIP provides primary care practices with detailed instructions on how to implement, monitor and report on an ACP Quality Improvement initiative. Importantly, the ACP QIP provides guidance and practical tools for developing objectives, establishing targets, and identifying measures and baselines for performance. CCO is actively promoting the ACP QIP in an effort to encourage uptake and broad adoption across Ontario. Results: There is now evidence that with ACP there is a greater likelihood EOL wishes will be both known and followed resulting in improved EOL care. ACP is also associated with decreased distress among the family members. Conclusions: Creating an ACP QIP supports primary care’s focus on advancing quality patient care. Importantly, implementing the ACP QIP into primary care practices has the potential to improve EOL care and secondarily reduce health care costs ultimately working towards achieving the triple aim of “better care, better health, and lower costs”.

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)0.021
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.347
Threshold uncertainty score0.690

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0040.009
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0250.007

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 head0.248
GPT teacher head0.603
Teacher spread0.355 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2014
Admission routes2
Has abstractyes

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