Evaluating the role of social media in providing support for family caregivers of individuals with spinal cord injury
Bibliographic record
Abstract
Abstract Study Design: Quantitative Study Objectives: The study aimed to explore Family Caregivers of Individuals with Spinal Cord Injury (FC-SCI) social media use patterns, most frequently used platforms, importance of social media for receiving and providing support, and type of social support (i.e., social companionship, emotional support, informational support) that FC-SCI receive or provide online. Setting: FC-SCI participants from Canada and USA. Methods: FC-SCI responded to measures regarding the social media platforms they use to access support, the importance of each platform, and the types of online social support they access through social media. Results: Sample consisted of 115 FC-SCI. Most caregivers were a partner or spouse of the individual with SCI (n=110) and female (n=111). Majority of FC-SCI spent 1-3 hours daily on social media (n=74), and Facebook was used predominantly (n=108) to access support. For receiving or providing support, Facebook was ranked most important (60%). The mean differences and stand deviation were found for the types of social support: emotional support (25.93 ± 7.60), social companionship (23.85 ± 7.46), and informational support (27.24 ± 7.50). Conclusions: Using social media for informational support is desired by FC-SCI as it is easily accessible, and time-efficient. The prevalent use of social media for support by FC-SCI demonstrates that social media, particularly Facebook, is a valued platform for support. The support benefits for the mental and physical health of caregivers should be further evaluated.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".