Advance-care planning quality improvement plan: A Cancer Care Ontario toolkit to support primary care teams to implement advance care plans in practice.
Bibliographic record
Abstract
76 Background: In Ontario, the Ministry of Health and Long Term Care’s (MOHLTC) uses Quality Improvement Plans (QIPs) to drive system improvement aimed at providing high value, high quality care for all. To support the introduction of QIPs into the primary care sector, Cancer Care Ontario has developed an Advance Care Planning (ACP) toolkit for practices that include ACP as part of their annual QIP. ACP is an ongoing and dynamic process that involves a capable individual reflecting on their current values and beliefs for their health care, communicating their personal wishes for future health care and identifying an individual who will make decisions on their behalf in the event that they are unable to provide informed consent. The process is iterative and wishes may change over time with changes in health status. Methods: The ACP QIP was developed based on the Plan, Do, Study, Act cycle of continuous quality improvement. The ACP QIP provides primary care practices with detailed instructions on how to implement, monitor and report on an ACP Quality Improvement initiative. Importantly, the ACP QIP provides guidance and practical tools for developing objectives, establishing targets, and identifying measures and baselines for performance. CCO is actively promoting the ACP QIP in an effort to encourage uptake and broad adoption across Ontario. Results: There is now evidence that with ACP there is a greater likelihood EOL wishes will be both known and followed resulting in improved EOL care. ACP is also associated with decreased distress among the family members. Conclusions: Creating an ACP QIP supports primary care’s focus on advancing quality patient care. Importantly, implementing the ACP QIP into primary care practices has the potential to improve EOL care and secondarily reduce health care costs ultimately working towards achieving the triple aim of “better care, better health, and lower costs”.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".