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How to evaluate an advance care planning policy – strategies for each stage of implementation

2012· article· en· W2321675398 on OpenAlexaffabout
TL. Wityk Martin, Bjørnar Berg, Jessica Simon

Bibliographic record

VenueBMJ Supportive & Palliative Care · 2012
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsAuditProcess managementPlan (archaeology)Scale (ratio)BusinessQuality managementImplementation researchBaseline (sea)Operations managementHealth careQuality (philosophy)Monitoring and evaluationEnvironmental resource managementPerformance indicatorNursingMedicineAccountingService (business)Political scienceMarketingEconomic growthEngineeringPsychological interventionEconomics

Abstract

fetched live from OpenAlex

In 2008, the “Advance Care Planning: Goals of Care Designation (Adult)” policy (ACP:GCD) was systematically implemented throughout the Calgary Zone of Alberta Health Services. A comprehensive evaluation plan was imbedded pre and post implementation across all health sectors. Key indicators were identified to measure policy outcomes. A diverse and flexible evaluation model was developed for differing stages of implementation. Evaluation successes and challenges will be outlined. The outcomes achieved demonstrate the successes of a comprehensive ACP policy framework. Our evaluation approach was critical to ensuring full policy integration. A large scale comprehensive chart audit was conducted across all sectors at baseline, 6 and 18 months post implementation; additional charts were reviewed as needed (total of 18669 charts). Key outcomes relating to policy implementation, transfer of information, and end of life care preferences being followed were achieved in all sectors. At 18 months, 4 out of 5 sectors had GCDs on at least 93% of their charts. GCDs transferred from acute care to facility living 84% of the time. 97% of GCDs were followed at end of life. Outcome data informed strategies for ongoing integration of the policy, areas for continued quality improvement and gaining and sustaining organisational support. Three years post implementation, a different approach to evaluation is required. Although data demonstrates increasing adherence to policy principles, areas for continued improvement were also identified. We will present targeted quality improvement activities being used to address gaps and barriers.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.238
GPT teacher head0.562
Teacher spread0.324 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

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