International perspectives on advance care planning
Bibliographic record
Abstract
Fraser Health Authority in British Columbia, Canada serves 1.5 million people, spanning over 12 communities with 26 000 employees and has been a provincial and national leader in advance care planning (ACP) for the past 7 years. Our goal is to continue to promote and enhance ACP across our health system by promoting conversations with patients and families and improving communication and documentation of decision making in all programs of Fraser Health. In June 2010, we embarked on the Talking it Thru: Medical Orders for Scope of Treatment initiative to further embed ACP practices into all programs with key clinicians and populations clearly identified. This presentation will assist learners to gain further understanding of the importance of systematic engagement in order to embed and sustain ACP in large healthcare organizations and to enhance standardized practices for quality patient-centred care at end of life. The End of Life program provides project leadership and supports programs as they assess current practices, identify gaps and opportunities and plan for implementation. Phase One of this 3 year project included engaging programs by identifying leadership, facilitating Focus Groups and helping program leadership plan for implementation. 16 Focus Groups were held with over 200 clinicians. Input from program areas such Aboriginal Health, Home Health, Primary Care and Medicine as well as discipline areas such as respiratory therapists, social workers and hospitalists have proven invaluable to help shape policy, develop tools and resources and to address educational needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".