Care management and care provision for older relatives amongst employed informal care-givers
Bibliographic record
Abstract
ABSTRACT This paper examines care management, or ‘managerial care’, a type of informal care for older adults that has been relatively neglected by researchers. While previous research has acknowledged that care-giving may involve tasks other than direct ‘hands-on’ care, the conceptualisation of managerial care has often been vague and inconsistent. This study is the first explicitly to investigate managerial care amongst a large sample of carers. In our conceptualisation, care management includes care-related discussions with other family members or the care recipient about the arrangements for formal services and financial matters, doing relevant paperwork, and seeking information. The study examines the prevalence of this type of care, the circumstances under which it occurs, its variations by care-giver characteristics, and its impact on the carers. We drew from the Canadian CARNET ‘Work and Family Survey’ a sub-sample of 1,847 full-time employed individuals who were assisting older relatives. The analysis shows that managerial care is common, distinct from other types of care, a meaningful construct, and that most care-givers provide both managerial and direct care. Care management includes both the orchestration of care and financial and bureaucratic management. Providing managerial care generates stress amongst women and interferes with work amongst men, and the aspect that generates the greatest personal and job costs amongst both men and women is the orchestration of care.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".