Online peer mentorship programmes for children and adolescents with neurodevelopmental disabilities: A systematic review
Bibliographic record
Abstract
BACKGROUND: Children and adolescents with neurodevelopmental disabilities may be less well integrated into their community than their peers. Online groups can be particularly accessible for individuals with neurodevelopmental disabilities, as individuals may be able to connect with a larger network than they would in their local community. This systematic review aimed at estimating the effectiveness of online peer mentorship programmes on children and adolescent's participation in life situations. METHODS: A systematic review was conducted to search Medline, PsycINFO, Embase, CINAHL, and Education Research Complete (ERIC) electronic databases. Thematic analysis was done for studies that used qualitative methodology. RESULTS: Eleven articles were included, and they examined the influences of five different structured online peer mentorship intervention programmes and six different online support groups. The disabilities included cerebral palsy (n = 3), autism spectrum disorder (n = 3), spina bifida (n = 2), attention deficit hyperactivity disorder (n = 2), and other neurodevelopmental disorders. The mentors included in the studies were caregivers of children with disabilities, youth and adults with disabilities, and a virtual peer actor. The mentees included in the studies were youth with disabilities (age 10-19 years) and their families. Intervention characteristics varied across the studies but consistently showed a unique potential to facilitate social networking and support. Intervention programmes with specific content and structure showed better participation outcomes than unstructured interventions. Presence of a moderator and participant characteristics (age and sociocultural background) was suggested to influence the outcomes of interventions. CONCLUSIONS: Online peer mentorship programmes appear to have positive influence on social engagement and participation in life situation for children and adolescents with disabilities. This paper discusses several areas that should be considered in future research studies to improve potential effectiveness and use of study designs that help to establish not only if interventions work but also for whom they work best and why.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".