Evaluating iSibWorks: A virtual cognitive-behavioural intervention for siblings of children with disabilities
Bibliographic record
Abstract
1) Examine if participation in iSibWorks, a group-based virtual intervention for siblings of children with disabilities, impacted siblings' perception of quality of life (QoL) and social support; and 2) Explore siblings' feedback on iSibWorks. Thirty-eight children participated in iSibWorks and completed questionnaires (Pediatric Quality of Life [PedsQL™], Social Support Scale for Children [SSSC]) one week pre- and post-intervention. Conventional content analysis was used to explore siblings' open-ended responses on a post-participation feedback form. No significant differences in PedsQL™ and SSSC scores were observed after participating in iSibWorks. Despite this, siblings had positive feedback about iSibWorks and discussed: 1) Engaging in group learning and activities, 2) Meeting other siblings, and 3) Applying iSibWorks content to their daily life. Factors related to the COVID-19 pandemic such as family stress, school closures, virtual learning, and social distancing likely impacted study results. Although there were no significant changes in QoL and social support, siblings found iSibWorks to be fun, meaningful, and engaging. Siblings of children with disabilities can experience psychosocial challenges and there are few virtual interventions designed for this population. iSibWorks was adapted to address this gap and increase access and support for siblings of children with disabilities.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".