Poster (Knowledge Generation) ID 1969461
Bibliographic record
Abstract
Background Individuals with SCI often receive care from their family members. Caregiving responsibilities lead to caregiver burden. Receiving social support may mitigate the negative impacts of caregiving. Caregivers can obtain support from people they meet in-person, such as family members and people with whom they interact through social platforms. Objectives Our objective was to investigate the moderating effect of in-person and online social supports on the association between relationship quality, caregiver competence, caregiver distress and caregiver burden. Methods/Overview 115 caregivers who resided in Canada or the United States, understood and spoke English, and self-identified as primary family caregivers of an individual with SCI were sampled. Participants completed measures assessing relationship quality, competence, distress, burden, and in-person and online social support. Three separate moderation analyses for each outcome variable (i.e. relationship quality, competence, and distress), were conducted. In analyses, burden was the predictor, and online and in-person support were moderators. Results Moderation analyses showed that online support moderates the link between caregiver burden and distress. Slope analyses revealed that the positive relationship between burden and distress was weaker when caregivers reported lower levels of online support (p=0.005); this relationship was more robust when caregivers reported higher levels of online support (p <.001). Other moderation analyses were not statistically significant. Conclusions Online support increases distress in family caregivers. It is likely that comparing their life with other caregivers or being exposed to other caregivers’ grief negatively affects caregivers. More research is needed to understand how online support impacts caregivers negatively.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.973 | 0.896 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".