Applying the Knowledge-to-Action Framework to Engage Stakeholders and Solve Shared Challenges with Person-Centered Advance Care Planning in Long-Term Care Homes
Bibliographic record
Abstract
As they near the end of life, long term care (LTC) residents often experience unmet needs and unnecessary hospital transfers, a reflection of suboptimal advance care planning (ACP). We applied the knowledge-to-action framework to identify shared barriers and solutions to ultimately improve the process of ACP and improve end-of-life care for LTC residents. We held a 1-day workshop for LTC residents, families, directors/administrators, ethicists, and clinicians from Manitoba, Alberta, and Ontario. The workshop aimed to identify: (1) shared understandings of ACP, (2) barriers to respecting resident wishes, and (3) solutions to better respect resident wishes. Plenary and group sessions were recorded and thematic analysis was performed. We identified four themes: (1) differing provincial frameworks, (2) shared challenges, (3) knowledge products, and 4) ongoing ACP. Theme 2 had four subthemes: (i) lacking clarity on substitute decision maker (SDM) identity, (ii) lacking clarity on the SDM role, (iii) failing to share sufficient information when residents formulate care wishes, and (iv) failing to communicate during a health crisis. These results have informed the development of a standardized ACP intervention currently being evaluated in a randomized trial in three Canadian provinces.
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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 | 0.081 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.023 | 0.051 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".