The Impact of Advance Care Planning on Healthcare Professionals’ Well-being: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Advance care planning (ACP) improves care for patients with chronic illnesses and reduces family stress. However, the impact of ACP interventions on healthcare professionals' well-being remains unknown. OBJECTIVE: To systematically review the literature evaluating the impact of ACP interventions on healthcare professionals' well-being. METHODS: We followed the Joanna Briggs Institute methodology for systematic reviews and registered the protocol in PROSPERO (CRD42022346354). We included primary studies in all languages that assessed the well-being of healthcare professionals in ACP interventions. We excluded any studies on ACP in psychiatric care and in palliative care that did not address goals of care. Searches were conducted on April 4, 2022, and March 6, 2023 in Embase, CINAHL, Web of Science, and PubMed. We used the Mixed Methods Appraisal Tool for quality analysis. We present results as a narrative synthesis because of their heterogeneity. RESULTS: We included 21 articles published in English between 1997 and 2021 with 17 published after 2019. All were conducted in high-income countries, and they involved a total of 1278 participants. Three reported an interprofessional intervention and two included patient partners. Studies had significant methodological flaws but most reported that ACP had a possible positive impact on healthcare professionals' well-being. CONCLUSION: This review is the first to explore the impact of ACP interventions on healthcare professionals' well-being. ACP interventions appear to have a positive impact, but high-quality studies are scarce. Further research is needed, particularly using more rigorous and systematic methods to implement interventions and report results.
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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 | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".