Factors related to social disconnectedness among older unpaid caregivers
Bibliographic record
Abstract
Background Older unpaid caregivers often face social isolation and loneliness, yet risk factors for social disconnection remain largely unexplored. As the demand for unpaid caregiving rises with an aging population, there is a need for targeted interventions to reduce social disconnectedness in this vulnerable group. This study aimed to identify determinants of social disconnectedness. Methods Data came from a sample of 701 unpaid caregivers aged 60 + who completed an internet-based survey assessing sociodemographics, health status, financial strain, social environment, and social disconnectedness. Four sequential regression models were used to identify the unique contribution of these factors related to social disconnectedness. Results The first model (F = 3.94, p < 0.001, aR2 = 0.030) showed that older age (β = −0.15, p < 0.001), self-identifying as being Black (β = −0.10, p = 0.008), and higher education (β = −0.11, p = 0.041) were associated with lower social disconnectedness. Adding health factors in the second model (F = 15.33, p < 0.001, aR2 = 0.170) revealed that, in addition to age and education, chronic conditions (β = 0.12, p = 0.001) and possible depression (β = 0.35, p < 0.001) were associated with social disconnectedness. Including financial strain in the third model (F = 15.52, p < 0.001, aR2 = 0.212) showed that household income (β = −0.10, p = 0.012) and financial stress (β = 0.18, p < 0.001) were additionally associated with social disconnectedness. The final model (F = 23.42, p < 0.001, aR2 = 0.366) that included social environmental factors showed that age (β = −0.07, p = 0.033), possible depression (β = 0.22, p < 0.001), financial stress (β = 0.16, p < 0.001), and levels of community belonging (β = −0.20–0.58, p < 0.001) were significantly related to the risk of disconnectedness. Conclusion Findings highlight possible intervention targets that have the potential to reduce social disconnectedness among older unpaid caregivers. Particularly, addressing depressive symptoms, reducing financial stress, and enhancing community belonging are essential components to mitigate social disconnectedness risk in this population.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".