Role of Resilience in Mitigating Parental Stress of Parents Raising Children with Autism – A Bibliometric Analysis
Bibliographic record
Abstract
Although resilience is essential for reducing parental stress to the parents of children with Autism Spectrum Disorder (ASD), there are limited information on worldwide research trends and collaborations in this area. This study presents a bibliometric analysis of literature published from 2020 to 2024 on the aspect of resilience in mitigating parental stress of the parents raising autistic children. Data were retrieved from the Dimensions AI database (2020–2024) using the keywords “Resilience” AND “Parental Stress” AND “Autistic Children,” yielding 2,223 open-access articles. The dataset was analyzed using RStudio for performance metrics and VOS viewer for network visualization. The analysis identified key contributors (with Emily Simonoff and Sven Bölte emerging as the most influential authors), highly cited papers (e.g., Singh, 2020; Lord et al., 2020; Chiarotti & Venerosi, 2020; Asbury et al., 2020; Carter et al., 2020), and major publishing countries (USA, UK, China, Canada, Australia) and institutions (e.g., University College London, King’s College London, Harvard University, University of Toronto). Collaboration networks reveal extensive international partnerships, notably with India serving as a frequent collaborator with top countries. Results indicate a growing global research interest in resilience as a buffer against parental stress in ASD contexts. Resilient families tend to report lower stress levels, and international collaborations are strengthening the knowledge base on this topic. These findings help map the current scholarly landscape and suggest a particular way for future research and practice to better support to the parents of children with ASD, predicting treatment outcomes, and developing new antibiotic therapies. In conclusion, the potential of AI/ML to improve MDR management and treatment outcomes is significant.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.180 | 0.175 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".