‘Not yet’ and ‘Just ask’: barriers and facilitators to advance care planning—a qualitative descriptive study of the perspectives of seriously ill, older patients and their families
Bibliographic record
Abstract
OBJECTIVES: To explore seriously ill, older hospitalised patients' and their family members' perspectives on the barriers and facilitators of advance care planning (ACP). METHODS: We used qualitative descriptive study methodology to analyse data from an interviewer administered, questionnaire-based, Canadian multicentre, prospective study of this population. RESULTS: Three main categories described these barriers and facilitators: (1) person (beliefs, attitudes, experiences, health status), (2) access (to doctors and healthcare providers, information, tools and infrastructure to communicate ACP preferences) and (3) the interaction with the doctor (who and how initiated, location, timing, quality of communication, relationship with doctor). CONCLUSIONS: Based on the findings, we suggest strategies for both healthcare systems and individual healthcare providers to improve the quality and quantity of ACP with this population. These include assessing readiness for participation in ACP and personalising relevance of ACP to each individual, routinely offering scheduled family meetings for exploring a person's own goals and sharing information, ensuring systems and policies are in place to access previous ACP documentation and ensuring doctors' education includes ACP communication skills.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".