Employment and family caregiving in palliative care: An international qualitative study
Bibliographic record
Abstract
BACKGROUND: Family caregivers provide the majority of palliative care. The impact of family caregiving on employment and finances has received little research attention in the field of palliative care. AIM: The aim of this study was to explore perspectives and experiences of combining paid employment with palliative care family caregiving, and to assess the availability and suitability of employment support across three countries - the United Kingdom (UK), Aotearoa New Zealand and Canada. DESIGN: = 9). Interviews were recorded, transcribed and analysed using the principles of thematic analysis. RESULTS: Four main themes were identified: (1) significant changes to working practices are required to enable end of life family carers to remain in work; (2) the negative consequences of combining caregiving and employment are significant, for both patient and carer; (3) employer support for working end of life caregivers is crucial but variable and; (4) national, federal and government benefits for working end of life family carers are necessary. CONCLUSION: Supporting carers to retain employment whilst providing care has potential benefits for the patient at end of life, the caregiver, and the wider economy and labour market. Employers, policymakers and governments have a role to play in developing and implementing policies to support working carers to remain in employment.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".