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Record W4281694639 · doi:10.1177/02692163221089134

Employment and family caregiving in palliative care: An international qualitative study

2022· article· en· W4281694639 on OpenAlexaboutno aff
Clare Gardiner, Beth Taylor, Hetty Goodwin, Jackie Robinson, Merryn Gott

Bibliographic record

VenuePalliative Medicine · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
FundersUniversity of Sheffield
KeywordsPalliative careAotearoaThematic analysisNursingGovernment (linguistics)End-of-life careMedicineQualitative researchRespite careFamily caregiversFamily lifeFamily medicineSociologySocioeconomicsGender studies

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.431
Teacher spread0.341 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations14
Published2022
Admission routes1
Has abstractyes

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