Understanding the caring capacity of informal networks of frail seniors: a case for care networks
Bibliographic record
Abstract
Population ageing and constraints on public sector spending for older people with long-term health problems have led policy makers to turn to the social networks of older people, or the ‘informal sector’, as a source of long-term care. An important question arising from this policy shift is whether these social networks have the resources to sustain the high levels of care that can be required by older people with chronic health problems. In the face of both dire warnings about the imminent demise of the informal sector, and concurrent expectations that it will be the pillar of community long-term care, it is timely to undertake a critical analysis of the caring capacity of older people's social networks. In this paper we argue that the best way to understand the caring capacity of informal networks of frail older people is to establish their membership and caring capacity. It is useful to make conceptual distinctions between ‘social’, ‘support’, and ‘care-giving’ networks. We argue that transitions of networks from social through support to care roles are likely to show systematic patterns, and that at each transition the networks tend to contract as the more narrowly defined functions prevail. A focus on ‘care networks’, rather than the more usual ‘care dyads’, will move forward our understanding of the caring capacity of the informal sector, and also our ability to forge sound social and health policies to support those who provide 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.047 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".