Gender Differences in Social Support Received by Informal Caregivers: A Personal Network Analysis Approach
Bibliographic record
Abstract
Social support is an important predictor of the health of a population. Few studies have analyzed the influence of caregivers' personal networks from a gender perspective. The aim of this study was to analyze the composition, structure, and function of informal caregiver support networks and to examine gender differences. It also aimed to explore the association between different network characteristics and self-perceived health among caregivers. We performed a social network analysis study using a convenience sample of 25 female and 25 male caregivers. A descriptive analysis of the caregivers and bivariate analyses for associations with self-perceived health were performed. The structural metrics analyzed were density; degree centrality mean; betweenness centrality mean; and number of cliques, components, and isolates. The variability observed in the structure of the networks was not explained by gender. Some significant differences between men and women were observed for network composition and function. Women received help mainly from women with a similar profile to them. Men's networks were broader and more diverse and they had more help from outside family circles, although these outcomes were not statistically significant. Our results indicate the need to develop strategies that do not reinforce traditional gender roles, but rather encourage a greater sharing of responsibility among all parties.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".