Well-Being of Family Caregivers of Individuals with Spinal Cord Injury: The Moderating Effects of Online Versus In-Person Social Support
Bibliographic record
Abstract
Objective: Family members of individuals with spinal cord injury often take on caregiving responsibilities, which can lead to caregiver burden. One factor that can mitigate the adverse effects of caregiving, such as caregiver burden, is receiving social support. Caregivers can obtain support from people they meet in person (in-person support) and on social media platforms (online support). The current cross-sectional correlational design study investigated the moderating effect of in-person and online support on the association between relationship quality, caregiver competence, caregiver distress, and caregiver burden (dependent variables). Methods: Family caregivers of an individual with spinal cord injury (n = 115) completed an online survey assessing relationship quality, competence, distress, burden, and in-person and online supports. Results: Moderation analyses showed that the negative associations between relationship quality and physical burden (B = −0.58; p = 0.019) and caregiver competence and physical burden (B = −0.73; p = 0.013) were more pronounced at higher levels of online social support. Furthermore, the magnitude of the negative associations between relationship quality and emotional burden (B = −0.52; p < 0.001) and caregiver competence and emotional burden (B = −0.34, p = 0.012) were more pronounced at higher levels of in-person social support. Moderation analyses also revealed that the positive association between distress and social burden (B = 0.47; p = 0.029) and emotional burden (B = 0.26; p = 0.045) were stronger when caregivers reported higher levels of online support. Conclusions: In-person and online support can buffer some aspects of caregiver burden on caregiver well-being. While online support is usually considered beneficial, greater online engagement may contribute to higher levels of burden when the distress is high. It is possible, however, that caregivers who are more distressed engage more with online media to receive support.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".