Associations of discontinuation of care: A longitudinal analysis of the English Longitudinal Study of Ageing?
Bibliographic record
Abstract
Informal carers play a vital part to ensuring that individuals in need of care, due to illness or disability, continue to experience a good quality of life. Care provision has been studied extensively, however, little is known about the associations of discontinuing care. This knowledge is important not only to ensure that informal carers are supported, even after caring episode, but also to ensure the care-recipients are not left without support. By conducting longitudinal analysis of the English Longitudinal Study of Ageing (ELSA), this paper uniquely starts to unveil the associations of discontinuing the caring role and the patterns of care provision prior to discontinuing. A multivariable binary regression analysis was conducted of the ELSA waves 7 (2015) to 8 (2017), total sample size of N = 6,687. 10.5% (n = 701) respondents were identified as discontinued carers. The dependent variable was care provided in wave 7 but not in wave 8 (a discontinued carer), compared to providing care in both waves (a continuing carer). Socio-demographic, such as age, gender, marital status, economic activity and health acted and caring patterns, such as intensity and relationship with care-recipient, acted as independent variables. Results showed nearly 30% of discontinued carers had provided care to a spouse. Nearly a quarter of continuing carers had transitioned between care-recipients, which raises the question of a potential 'Serial Carer Trajectory'. The regression analysis noted that being divorced or widowed increased the odds of discontinuing care. Providing 20-49 hr of care per week was associated with lower odds of discontinuing the role. This paper suggests that policymakers should take a holistic approach to policies to support carers through all stages of their caring journey, including after discontinuing the caring role. This could ensure carers settle into life post-caring and continue to feel valued and recognised.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".