Advance Care Planning: Identifying System-Specific Barriers and Facilitators
Bibliographic record
Abstract
BACKGROUND: Advance care planning (acp) is an important process in health care today. How to prospectively identify potential local barriers and facilitators to uptake of acp across a complex, multi-sector, publicly funded health care system and how to develop specific mitigating strategies have not been well characterized. METHODS: We surveyed a convenience sample of clinical and administrative health care opinion leaders across the province of Alberta to characterize system-specific barriers and facilitators to uptake of acp. The survey was based on published literature about the barriers to and facilitators of acp and on the Michie Theoretical Domains Framework. RESULTS: Of 88 surveys, 51 (58%) were returned. The survey identified system-specific barriers that could challenge uptake of acp. The factors were categorized into four main domains. Three examples of individual system-specific barriers were "insufficient public engagement and misunderstanding," "conflict among different provincial health service initiatives," and "lack of infrastructure." Local system-specific barriers and facilitators were subsequently explored through a semi-structured informal discussion group involving key informants. The group identified approaches to mitigate specific barriers. CONCLUSIONS: Uptake of acp is a priority for many health care systems, but bringing about change in multi-sector health care systems is complex. Identifying system-specific barriers and facilitators to the uptake of innovation are important elements of successful knowledge translation. We developed and successfully used a simple and inexpensive process to identify local system-specific barriers and enablers to uptake of acp, and to identify specific mitigating strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".