BOS1b.004 The creation of an easy read advance care planning workbook
Bibliographic record
Abstract
Background As one of the largest and most diverse health authorities in Canada, Fraser Health Authority’s (FHA) Regional Advance Care Planning (ACP) Team is privileged to facilitate hundreds of education sessions and workshops for the public each year. We are committed to equity and are constantly striving to meet the varying needs and abilities of our communities and community partners. Methods Innovative Approach Results In collaboration with community partners, this team co-created a picture based, simplified Easy Read Advance Care Planning Workbook. It supports people who may process information differently. Target audiences include: People living with brain conditions such as Dementia, Huntington’s disease, ALS, Parkinson’s disease, or tumors People living with impairments from health events such as aphasia from Stroke or other limitations from Brain Injury People living with disabilities People with limited English literacy (when translation is not available) This is a first of its kind inclusive resource which includes simultaneous caregiver and support persons with guidance to assist others through the ACP process. Conclusion In this oral presentation, participants will: Discuss the importance and challenges of creating an inclusive workbook that meets the diverse needs and abilities of various underserved populations Review key steps and significant stakeholders involved in an inclusive equity-focused process Begin to explore ways to adapt this workbook into your country or community.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.202 | 0.103 |
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".