Why is advance care planning underused in oncology settings? A systematic overview of reviews to identify the benefits, barriers, enablers, and interventions to improve uptake
Bibliographic record
Abstract
Background: Advance care planning (ACP) centres on supporting people to define and discuss their individual goals and preferences for future medical care, and to record and review these as appropriate. Despite recommendations from guidelines, rates of documentation for people with cancer are considerably low. Aim: To systematically clarify and consolidate the evidence base of ACP in cancer care by exploring how it is defined; identifying benefits, and known barriers and enablers across patient, clinical and healthcare services levels; as well as interventions that improve advance care planning and are their effectiveness. Methods: A systematic overview of reviews was conducted and was prospectively registered on PROSPERO. PubMed, Medline, PsycInfo, CINAHL, and EMBASE were searched for review related to ACP in cancer. Content analysis and narrative synthesis were used for data analysis. The Theoretical Domains Framework (TDF) was used to code barriers and enablers of ACP as well as the implied barriers targeted by each of the interventions. Results: Eighteen reviews met the inclusion criteria. Definitions were inconsistent across reviews that defined ACP (n=16). Proposed benefits identified in 15/18 reviews were rarely empirically supported. Interventions reported in seven reviews tended to target the patient, even though more barriers were associated with healthcare providers (n=40 versus n=60, respectively). Conclusion: To improve ACP uptake in oncology settings; the definition should include key categories that clarify the utility and benefits. Interventions need to target healthcare providers and empirically identified barriers to be most effective in improving uptake. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?, identifier CRD42021288825.
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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 | 0.048 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".