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Record W4323039946 · doi:10.1101/2023.02.28.23286494

The Effectiveness of Advance Care Planning Training for Care Home Staff: a Systematic Review

2023· review· en· W4323039946 on OpenAlexaboutno aff
Victoria Barber-Fleming, Mala Mann, Gillian Mead, Aoife Gleeson

Bibliographic record

VenuemedRxiv · 2023
Typereview
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
FundersMarie Curie
KeywordsCINAHLPsychological interventionPsycINFOMedicineMEDLINENursingHealth careAdvance care planningAutonomySystematic reviewPopulationFamily medicinePalliative care

Abstract

fetched live from OpenAlex

Abstract In line with population ageing, the number of global deaths is predicted to increase. There have been projections that, within the next 20 years, in England and Wales, care homes may become the most common place of death. In order to respect the autonomy of their residents, it is therefore, vital that care home staff are able to have Advance Care Planning conversations. However, care home staff may lack the knowledge or confidence to have such discussions. Further, a systematic review found a paucity of evidence about whether Advance Care Planning training interventions for care home staff are effective. New, higher quality studies are now available, justifying this review update. We sought to address two questions: 1) ‘What Advance Care Planning education interventions exist for care home staff?’ and 2) ‘how effective are these interventions?’ All measurable outcomes of effectiveness (e.g. health system/resource-related, patient/relative-related, staff-related) including both qualitative and quantitative measures of effectiveness were considered. Design The review adheres to the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) and is registered on PROSPERO (ID: CRD42022337865). Original research evaluating Advance Care Planning education for care home staff and reporting any measurable outcome of effectiveness was included. We searched Ovid Medline All, Ovid Embase, Cochrane Central Register of Controlled Trials, EBSCO CINAHL, EBSCO ERIC, and Ovid PsycINFO from March 2018 (3 months prior to original review search cut-off) to June 2022, with supplemental journal and website searches. The results were synthesised by narrative synthesis. Findings The current review update almost doubled the number of included studies in a relatively short period. This review includes 10 studies (n = 310 care homes), from the UK, Belgium, Norway and Canada. UK studies were mainly related to the Gold Standard Framework for Care Homes. Two studies adopted multi-component education interventions. Outcome measures included resident/family, staff and health service-related concepts. Even after identifying a further 5 papers, there remains insufficient evidence to determine the effectiveness of Advance Care Planning education interventions for care home staff. Conclusions Advance Care Planning education interventions are heterogeneous and often complex in their design, flexibility, target populations, and outcomes. There remains insufficient data to determine the effectiveness of Advance Care Planning education interventions for care home staff, with a particularly urgent need to agree on outcome measures of the effectiveness. Future research could consider updating the existing Delphi consensus on outcome measures for evaluating Advance Care Planning, in light of this systematically collected evidence, with a view to agreeing outcomes that are specific to Advance Care Planning education interventions for care home staff.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.073
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.010
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.253
GPT teacher head0.499
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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