The Role of Informal Caregivers in Long-Term Care for Older People
Bibliographic record
Abstract
Abstract This chapter aims at describing and analyzing how the role of informal caregivers is reflected in terms of experiences and relationships, highlighting their needs and the response to them provided by different long-term care (LTC) systems. Informal caregivers represent an often invisible “pillar” of our welfare systems, albeit they outnumber the professional LTC workforce, both in terms of units and of overall economic value of the tasks they perform. Not few of them spend a great amount of time in providing assistance, especially when they live with the cared-for person. A gender-based analysis shows that, while women (who predominate in this role) are more likely to handle emotional support and personal care, men usually deal with financial and legal issues. A good integration between formal and informal care provision would play a crucial role to ensure an adequate quality and continuity of care. However, the availability of formal services—in terms of coverage, intensity and quality—varies largely across countries, following different approaches. To better capture this variety, this chapter first provides a conceptual framework integrating the main actors involved in LTC, including the emerging role of migrant care workers. Secondly, it describes in detail informal caregivers’ main needs and the support interventions which can address them. Finally, it closes with a few reflections on the overall role of informal care and its relation with some current social and cultural trends.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".