O-3 Scoping the financial support for family caregivers at end of life across six countries
Bibliographic record
Abstract
Background Informal caregivers of patients at the end of life can face significant financial burden. Some form of caregiver financial support is advised in most countries with developed healthcare systems. Whilst appropriate support can reduce the financial burden for informal caregivers and improve health and wellbeing, little is known about the scope of financial support for informal caregivers across countries with similarly developed healthcare systems. Aim To identify and compare sources of financial support for informal caregivers of those at the end of life across six countries with similarly developed healthcare systems. Methods A mapping exercise was undertaken in two phases. Phase one involved searches of grey literature and academic databases to identify sources of financial support in the UK, Ireland, New Zealand, Australia, USA and Canada. Phase two comprised a survey of experts across the six countries to confirm and extend the literature review findings, and gain further insight into financial support. A total of 57 responses were received from experts including patients, family carers, academics, policy-makers and health care professionals. Results Some form of financial support for caregivers was available in all six countries and included national and local support which was aimed at the carer or aimed at the patient and benefitted the carer. The type, extent and reach of financial support varied within and between the six countries and was administered by multiple agencies including healthcare providers, government and charities. Types of support included payments to cover prescription charges, transport costs, medical insurance fees, changes to employment and bereavement benefits. Conclusion The findings highlight some novel examples of financial support available to family carers, alongside numerous gaps and inconsistencies, both within and between counties. In doing so, this study demonstrates the need for policy-makers, as well as palliative care providers, to learn from other countries’ experiences.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.022 | 0.028 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".