Transcending health literacy: using videos to aid advance care planning and healthcare decision-making
Bibliographic record
Abstract
A critical aspect of advance care planning is patient understanding of how goals of care and related medical interventions reflect their values and wishes for current and future healthcare decisions. Research regarding the use of patient education videos portraying resuscitative, medical and comfort care is demonstrating increases in patient understandings of healthcare interventions and the determination of medically appropriate goals of care. The use of goals of care videos assists with transcending health literacy for patients and families involved in healthcare decision making, and highlights the process of engaging in meaningful conversations between families and healthcare providers. This workshop will present a patient education video series that is based on current research development and findings that use videos to improve patient understanding about goals of care. The videos have been created to align with the “Advance Care Planning: Goals of Care Designation (ACP/GCD) (Adult)” policy that was systematically implemented within Alberta Health Services, Calgary Zone in 2008. The series is intended to provide relevant information related to healthcare considerations and blends the questions of ‘why engage in ACP?’ with ‘what are the healthcare interventions to consider?’ The two-part video series includes: Understanding Goals of Care and Engaging in Advance Care Planning, These videos will be presented as tools to help bridge the gap between patient values and wishes and beneficial medical interventions, as they relate to the Goals of Care framework adopted by Calgary Zone.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".