Barriers to and enablers of advance care planning with patients in primary care: Survey of health care providers.
Bibliographic record
Abstract
OBJECTIVE: To identify barriers to and enablers of advance care planning (ACP) perceived by physicians and other health professionals in primary care. DESIGN: Cross-sectional, self-administered survey. SETTING: Ontario, Alberta, and British Columbia. PARTICIPANTS: Family physicians (n = 117) and other health professionals (n = 64) in primary care. MAIN OUTCOME MEASURES: Perceived barriers relating to the clinician, characteristics of patients, and system factors, rated on a 7-point scale from 0 (not at all) to 6 (an extreme amount), and enablers reported using an open-ended question. RESULTS: Between November 2014 and June 2015, questionnaires were returned by 72.2% (117 of 162) of family physicians and 68.8% (64 of 93) of the other health professionals. Physicians rated insufficient time, inability to electronically transfer the advance care plan across care settings, decreased interaction with patients near the end of life owing to transfer of care, and patients' difficulty understanding limitations and complications of treatment options as the highest barriers. Other health professionals additionally identified their own lack of knowledge and difficulty accessing the physician as barriers. Themes identified as enablers included greater public engagement, clinician attitudes, creating capacity for clinicians, integrating ACP into practice, and system and policy supports. CONCLUSION: In primary care, there are barriers to engaging patients in ACP at the patient, provider, and system levels that could potentially be addressed through the informed development of multifaceted interventions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".