A Telepsychology-Based Social Competence Intervention for Youth with Learning Disabilities and Mental Health Difficulties During the COVID-19 Pandemic
Bibliographic record
Abstract
Youth with learning disabilities (LDs) have a heightened risk for co-occurring mental health difficulties. The co-occurrence of LDs and mental health difficulties (LDMH) is associated with further risk of adverse impacts on cognitive and academic performance. Therefore, the availability of effective social competence interventions for youth with LDMH is essential to scaffold skill development and prevent cascading difficulties into adulthood. That said, the onset of the COVID-19 pandemic led to the immediate pause of most in-person therapeutic services. In response to worsening of youths’ mental health difficulties and the significant challenges that the pandemic created for mental health service delivery, the Child Development Institute in Ontario, Canada, transitioned their in-person Social Awareness, Competence, Engagement, & Skills (ACES) intervention service to virtual implementation. I conducted two studies with the purpose of gathering qualitative and quantitative data to examine the feasibility, acceptability, and effectiveness of the telepsychology-based adaptations of Social ACES from the clinician, caregiver, and youth perspectives. Methods: Data collection occurred through in-depth semi-structured interviews of nine Social ACES clinicians (Study 1), four caregivers and four youth who partook in the telepsychology-based intervention (Study 2); lived experiences were analysed using the qualitative approach of Interpretative Phenomenological Analysis. For a mixed-methods perspective, I also examined outcomes of the intervention through quantitative parent ratings of their child’s social competence pre- post-treatment, augmented by clinicians’ reports (Study 2). The data was triangulated to provide a deeper perspective of the youths’ progress through the program and challenges experienced. Results: The findings resulted in the emergence of four (Study 1) and two (Study 2) major themes, as well as elucidating four integrated youth case studies, to help clarify clinicians’, caregivers, and youths’ perceptions of the adaptation. Conclusions: These studies provided preliminary evidence for the feasibility, acceptability, and effectiveness of virtual Social ACES. The findings have implications for the future of mental health service delivery, raise further questions about the effectiveness of social competence programming during and after a time of significant disruption, and point to several lines of inquiry for future critical research on virtual interventions for children and youth.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".