PP19.002 Developing an interactive advance care planning framework
Bibliographic record
Abstract
Background Fraser Health Authority (FHA) has had an Advance Care Planning (ACP) Framework since 2017. The model served as a guide to support health care professionals (HCPs) expand their understanding and support evidenced based practice. It supported HCPs with questions such as: When do I start ACP conversations with clients? Where do I document these conversations? What kinds of questions should I be asking clients? What is the link between ACP and Medical Orders for Scope of Treatment (MOST)? What other HCP resources are available for me and for clients? However, a traditional text-based structure is not adequate for the current needs of busy HCPs in 2022. Methods Innovative Approach Results Together with provincial partners, the Regional FHA ACP team co-created an interactive illustrative framework and an accompanying toolkit to support consistent understanding of ACP. Conclusion While the nautical graphics assist with ‘big picture’ understanding, the complementary HCPs toolkit outlines ‘what to do and when’. Both contain clickable direct links to clinician tools, documentation forms, contact information for advice, and informational resources to pass on to clients. The intention of this two-part framework is to address: A variety of ways of learning: visual (professionally designed graphics; links to videos), auditory (converted to pdf for pdf readers, links to podcasts), read/write (text based information), kinaesthetic (examples of conversations and documentation). Practicality: quick, need to know information. Links to exactly what they need, when they need it.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.085 |
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".