Mental health in children with disabilities and their families: red flags, services' impact, facilitators, barriers, and proposed solutions
Bibliographic record
Abstract
Background: Children and youth with neurodevelopmental disabilities (NDDs) and their caregivers are at a high risk of experiencing mental health challenges, that in turn can significantly affect their functioning, productivity, and quality of life. In this already vulnerable population, mental health difficulties are now more frequently reported and pronounced secondary to the isolation and uncertainties experienced during the pandemic. Our previous work has shown important mental health services' gaps for children/youth with NDDs and their families, highlighting the need to optimize and tailor existing practices. Objective: To explore mental health services' barriers, facilitators, impact, and solutions from the perspectives of HCPs and CGs, and to describe common precursors to mental health challenges in children with NDDs from the perspectives of these two groups. Methods: In a triangulation mixed-method study design embedding quantitative and qualitative approaches, participants completed a survey and a semi-structured interview. Descriptive statistics and a hybrid inductive/deductive thematic approach were used for data analysis. Results: = 7 categories) were identified and included reported feelings/perception of self, behavioral and physical manifestations, emotional dysregulation, and school-related factors, among others. Clinicians reported a widespread need for pediatric, family-centered mental health services and conveyed lacking mental health resources/training to meet the demand. Caregivers indicated being only moderately satisfied when care was received. Salient facilitators identified by clinicians were having an interdisciplinary team and caregiver's engagement in the therapeutic processes. Participants recommended improvements to increase accessibility to mediate the existing discrepancy between the emergence of precursors and care received; that services must target a broader population and be more comprehensive (e.g., family-centered care, addressing high-risk transition periods); and training/toolkits to support clinicians' evidence-based practice. Conclusion: Our findings emphasize the necessity of a systematic and standardized approach to mental health services for children with NDDs and their families. Enhancing caregiver support, addressing barriers, and adopting a proactive, family-centered approach are crucial for improving accessibility and quality. These proposed solutions provide valuable insights for shaping policies and practices in pediatric mental health services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".