Befriending carers of people with dementia: a cost utility analysis
Bibliographic record
Abstract
OBJECTIVE: There is very little evidence on the cost-effectiveness of social care interventions for people with dementia or their carers. The BEfriending and Costs of CAring trial (BECCA, ISRCTN08130075) aimed to establish whether a structured befriending service improved the quality of life of carers of people with dementia, and at what cost. METHODS: We performed an economic evaluation alongside a single blind, randomised controlled trial in a community setting of 236 carers of people with a primary progressive dementia. The intervention was contact with a Befriender Facilitator (BF), and offer of match with a trained lay volunteer befriender compared with no BF contact. Main outcome measures were health and social care, voluntary sector, and family care costs and quality adjusted life years (QALYs) in carers over 15 months. RESULTS: Mean QALYs per carer over 15 months were 0.017 higher in the intervention group compared with control (95%CI: -0.051, 0.083). Mean costs from a societal perspective were pound 1,813 higher (- pound 11,312, pound 14,984). The point estimate Incremental Cost Effectiveness Ratio (ICER) is thus pound 105,954 per incremental QALY gained. Probabilistic sensitivity analysis suggests a 42.2% probability that the ICER is below pound 30,000 per QALY. Inclusion of dementia patient QALYs reduces the ICER to pound 28,848 (51.4% probability below pound 30,000). CONCLUSIONS: Befriending leads to a non-significant trend towards improved carer quality of life, and there is a non-significant trend towards higher costs for all sectors. It is unlikely that befriending is a cost-effective intervention from the point of view of society.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".