Whose job? The staffing of advance care planning support in twelve international healthcare organizations: a qualitative interview study
Bibliographic record
Abstract
BACKGROUND: ACP involving a facilitated conversation with a health or care professional is more effective than document completion alone. In policy, there is an expectation that health and care professionals will provide ACP support, commonly within their existing roles. However, the potential contributions of different professionals are outlined only broadly in policy and guidance. Research on opportunities and barriers for involving different professionals in providing ACP support, and feasible models for doing so, is currently lacking. METHODS: We identified twelve healthcare organizations aiming to offer system-wide ACP support in the United States, Canada, Australia and New Zealand. In each, we conducted an average 13 in-depth interviews with senior managers, ACP leads, dedicated ACP facilitators, physicians, nurses, social workers and other clinical and non-clinical staff. Interviews were analyzed thematically using NVivo software. RESULTS: Organizations emphasized leadership for ACP support, including strategic support from senior managers and intensive day-to-day support from ACP leads, to support staff to deliver ACP support within their existing roles. Over-reliance on dedicated facilitators was not considered sustainable or scalable. We found many professionals, from all backgrounds, providing ACP support. However, there remained barriers, particularly for facilitating ACP conversations. A significant barrier for all professionals was lack of time. Physicians sometimes had poor communication skills, misunderstood medico-legal aspects and tended to have conversations of limited scope late in the disease trajectory. However, they could also have concerns about the appropriateness of ACP conversations conducted by others. Social workers had good facilitation skills and understood legal aspects but needed more clinical support than nurses. While ACP support provided alongside and as part of other care was common, ACP conversations in this context could easily get squeezed out or become fragmented. Referrals to other professionals could be insecure. Team-based models involving a physician and a nurse or social worker were considered cost-effective and supportive of good quality care but could require some additional resource. CONCLUSIONS: Effective staffing of ACP support is likely to require intensive local leadership, attention to physician concerns while avoiding an entirely physician-led approach, some additional resource and team-based frameworks, including in evolving models of care for chronic illness and end of life.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".