Social Support and Caregiving in Italy: The Specificities of Care Relationships
Bibliographic record
Abstract
Informal caregiving is an invisible component of familistic welfare regimes, where the burden of care is predominantly delegated to primary networks. Drawing on a structural interactionist perspective, this article presents findings from an Italian national survey of 1,504 adults, analysed through personal network methods to investigate how network morphology shapes caregiving practices. Caregivers (19.8% of the sample) assist older people, persons with chronic illnesses, or persons with disabilities. Results show that caregivers belong to larger and denser networks than non-caregivers, reflecting strong bonding capital typical of familistic contexts; however, they display lower betweenness and ego-centric density, signalling limited brokerage capacity and reduced access to bridging ties. This structural closure reinforces the “total social fact” nature of caregiving, where mixed tasks of physical and administrative care predominate. The most significant of these is the fact that for all types of frailty, over a quarter of carers say they have no one to support them in their caregiving activities. Despite a certain uniformity across caregiving profiles, differences emerge: disability care is embedded in cohesive, inward-looking networks associated with higher burden; chronic illness care mobilises more open networks and higher satisfaction; elder care remains rooted in normative familial obligations. Across conditions, over one quarter of caregivers report lacking any support, while dissatisfaction with formal services highlights a dualised care regime unable to compensate for weak bridging social capital. These findings underscore the need for policies that expand caregivers’ relational opportunities beyond primary networks. At the macro level, it does not seem necessary to distinguish policies by caregiver type. However, at the level of social intervention, it is considered appropriate to pay attention to some of the differences that emerged across the three profiles, such as the structure of their support networks, attitudes towards services, and respondents’ future projections in their role as caregivers.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".