Conversations that count: Advance Care Planning as preventative medicine
Bibliographic record
Abstract
Background: Fundamental to the concept of Advance Care Planning (ACP) is empowering individuals and communities to recognise death as an inevitable part of life.Methods: ACP facilitators and clinical champions in the Canterbury region of New Zealand have been very active in engaging the community. This has occurred through consumer presentations, the creation of specific pages on the Canterbury District Health Board (CDHB) consumer information website (HealthInfo) and support of the National ACP awareness campaign ‘Conversations that Count’.Results: ‘Consumer power’ has been invaluable in driving the uptake of ACP in the CDHB. A survey of 49 GPs in 2015 found many were reluctant to start ACP conversations or felt they ‘did not have time’. The turning point was the realisation that patients are not only wanting but are actively asking to have these important conversations and to create Advance Care Plans (ACPlans). 1200 electronic ACPlans have now now been created in Canterbury, with 80% generated in primary care.The ACP pages on HealthInfo are consistently in the top 20 pages viewed each month which indicates that the community is seeking information and wanting to take control. Uptake and demand for consumer presentations and ‘Conversations that Count’ resources also continues to grow year on year.Discussion: Increased awareness and understanding of ACP gives people the opportunity to think and frame their reasoning, so they are better prepared to have well informed discussions with health care professionals. It helps them be clearer in their mind about their own limits and concerns. It is important for people to consider the question - “what is O.K for me and what isn’t?”. In this context, ACP conversations can be seen as preventative medicine.Patients need to be as well equipped as possible to be active participants in healthcare decisions, especially regarding end of life. Through the ACP process, unnecessary suffering, confusion and conflict can be reduced or prevented and unwanted or burdensome treatment that is not in line with their goals and priorities can be averted.Conclusion: Valuing and honouring a person’s participation in their health care decision-making is important for all healthcare organisations. Prioritising ACP is an effective way of making this happen.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 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".