Organization of rehabilitation services for youth with physical disabilities and mental health problems: A scoping review
Bibliographic record
Abstract
Introduction: Youth with childhood-onset physical disabilities receiving rehabilitation services often present with many complex needs. Emerging evidence confirms co-occurrence of mental health problems in this population is common, and mental health is often overlooked during rehabilitation for chronic physical conditions. For example, symptoms of depression and anxiety are frequently present in adolescents with physical disability such as spina bifida or Duchenne muscular dystrophy, and access to mental health services is often limited. Addressing mental health concerns for this age group is particularly critical as it encompasses a challenging transition to adulthood. Objectives: Building upon findings from a recent scoping review on the co-occurrence of physical disabilities and mental health problems, this paper synthesizes scientific literature related to the organization and delivery of services for youth with co-occurring childhood-onset physical disabilities (e.g., cerebral palsy, spina bifida) and mental health problems (e.g., depression, anxiety). Methods: A scoping review protocol stemming from Arksey & O'Malley's framework and updated guidelines from the Joanna Briggs Institute was developed. Four databases (Medline, PsycINFO, CINAHL, Embase) were searched. The search was limited to French or English peer-reviewed articles published between 2000 and 2021. Articles included were primary papers addressing: 1) youth aged 15 to 24 with a childhood-onset physical disability, 2) mental health problems, and 3) healthcare service organization or delivery. They were screened by two reviewers and discussed with a third to establish consensus on the inclusion criteria and resolve disagreements. Results: Sixteen articles were retained from the 1,010 screened. Many (9/16) were from the United States. Two models were found: the Biopsychosocial, Collaborative, Agency-Based Service Integration Approach (including psychiatry in a pediatric rehabilitation hospital) and the Client Network Consultation (an interagency collaboration in mental health care for children with complex healthcare needs). Twelve key principles for service organization and delivery were identified and categorized into: collaboration and coordination, training and support, and delivery of care. Conclusion: Identified principles can guide improved service delivery for this population. Highlighted research gaps include the need for developing models of collaborative healthcare delivery and subsequently evaluating their effectiveness.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".