The Effectiveness of Advance Care Planning (ACP) Training for Care Home Staff: An Updated Systematic Review
Bibliographic record
Abstract
Context: Population ageing and projections that more people will die in care homes demand that care home staff are prepared for advance care planning (ACP). This is an update of a prior review, published in 2021, of ACP education interventions for healthcare professionals in care homes. Objective: We sought to address the questions: (1) What ACP education interventions exist for care home staff? and (2) How effective are these interventions? Method: The review adheres to PRISMA; PROSPERO (ID: CRD42022337865). Original research evaluating ACP education for care home staff, reporting any measurable outcome of effectiveness, was included. Extensive literature searches were performed from March 2018 to June 2022. The results were reported by narrative synthesis. Findings: We identified 10 studies (310 care homes), from the UK, Belgium, Norway and Canada. Major sources of heterogeneity between studies include intervention design, target population and outcome measure. More recent interventions target the wider multi-disciplinary team. There is a trend towards the adoption of more resident/family and staff-related outcomes. There was insufficient evidence to draw conclusions about the effectiveness of ACP education interventions. Limitations: Heterogeneity of the primary studies did not allow for meta-analysis. Implications: There is still insufficient data to determine the effectiveness of ACP education interventions for care home staff. Future researchers should aim to agree on outcomes that are specific to ACP education interventions for care home staff and develop standardised, validated outcome measures. Study design should consider an intervention’s ‘theory of change’ when considering outcomes.
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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 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".