How to build resiliency in autistic individuals: an implication to advance mental health
Bibliographic record
Abstract
INTRODUCTION: Individuals on the autism spectrum (ASD) often experience poor mental health and coping strategies compared to their peers due to social exclusion and co-occurring conditions. Resiliency has been identified as a key factor in preventing adverse outcomes and promoting mental health. Therefore, it is important to determine what strategies can be used to build resiliency among autistic individuals. The current paper is one of the first studies that aims to collect information from autistic individuals and their caregivers on potential strategies to enhance resiliency. METHODS: We interviewed 18 participants from various provinces in Canada, comprising of 13 autistic individuals and 5 parents. We used thematic analysis to identify patterns in the data. RESULTS: Thematic analysis revealed three themes to indicate strategies that could be used to enhance resiliency, including: (a) self-reliant strategies, (b) using community-based facilities, and (c) contextual and individual characteristics. CONCLUSION: Although the body of literature on resiliency is evolving, this paper provides a unique perspective as it is one of the few studies that considers the experiences of individuals on the spectrum. In addition, this study focuses on identifying and describing specific strategies that can be used to enhance resiliency and mental health, which consequently can help address the existing gaps in knowledge and practice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".