Mental health services and resources for children with developmental disabilities and their families: scan of local practices, gaps, and opportunities created
Bibliographic record
Abstract
Background Mental health concerns in children with disabilities are common and have a significant and negative impact. Clinicians have reported high demand for this population to receive early, targeted, and family-centred mental health interventions. Objective We sought to map out and describe existing pediatric mental health services/resources for children with disabilities and their families across clinical sites and local and online communities. Methods Using a mixed-method triangulation study design, we outreached to clinical managers at the participating clinical sites and conducted a rapid online search of local in-person, telehealth, and web-based information. The nature, access method, admission criteria, target, focus, and other pertinent information were recorded and analyzed using descriptive statistics and a narrative synthesis approach. Results Eighty-one (n = 81) services/resources (in-person, n = 48; telehealth, n = 10; web-based information, n = 33) were identified. Few (n = 6, 13%) in-person services had a method of care access through an online booking portal. Nearly half of in-person resources (n = 23, 47%) had admission criteria specific for children with disabilities (e.g., diagnosis, age limit), and many (n = 32, 67%) required a formal referral. A small number of in-person and telehealth services targeted the mental health concerns of the entire family (n = 23, 47%; n = 2, 20%). Very few (n = 13, 16%) services incorporated follow-up support. Important gaps emerged for certain populations (e.g., children with cerebral palsy). Practitioners’ inadequate training when intervening with co-existing mental health demands of children with disabilities was noted by clinical managers. Conclusion Findings could be used to create a user-friendly database to easily identify suitable services and to advocate for services/resources that are lacking.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.007 |
| 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".