Supporting lay carers in end of life care: current gaps and future priorities
Bibliographic record
Abstract
Informal carers are central to the achievement of end of life care and death at home and to policy aims of enabling patient choice towards end of life. They provide a substantial, yet hidden contribution to our economy. This entails considerable personal cost to carers, and it is recognised that their needs should be assessed and addressed. However, we lack good research evidence on how best to do this. The present position paper gives an overview of the current state of carer research, its gaps and weaknesses, and outlines future priorities. It draws on a comprehensive review of the carer literature and a consensus meeting by experts in the field. Carers' needs and adverse effects of caregiving have been extensively researched. In contrast, we lack both empirical longitudinal research and conceptual models to establish how adverse effects may be prevented through appropriate support. A reactive, "repair" approach predominates. Evaluations of existing interventions provide limited information, due to limited rigour in design and the wide variety in types of intervention evaluated. Further research is required into the particular challenges that the dual role of carers as both clients and providers pose for intervention design, suggesting a need for future emphasis on positive aspects of caregiving and empowerment. We require more longitudinal research and user involvement to aid development of interventions and more experimental and quasi-experimental research to evaluate them, with better utilisation of the natural experiments afforded by intra- and international differences in service provision.
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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 | 0.051 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".