A Scoping Review of Mental Health Programs for Parents of Children with Complex Medical Conditions
Bibliographic record
Abstract
Parents of children with complex medical conditions (CMC) are at-risk of mental health concerns but have few mental health interventions available. This study summarized extant research on mental health interventions for parents of children with CMC. Searches of PsycINFO, Medline, CINAHL, Web of Science, PubMED, and Social Services Abstracts databases occurred, and articles were screened using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines as well as the Arksey and O’Malley’s five-stage method for scoping reviews. Screening eligibility included being peer-reviewed and written in English, assessing a parent/caregiver of a child aged 0-12 with a CMC who received a mental health intervention, and published between January 1, 2000 to May 19, 2020. Nine studies were included, each assessing mental health, process, and/or feasibility outcomes. Reduced depression, anxiety, stress, and an increase in quality of life, psychological flexibility, mindfulness, coping skills, and shared management were found; however, results were mixed. Parental support/education and parent education/child developmental supports are important for treating stress and quality of life, respectively. Stress interventions are most effective when provided close to CMC diagnosis. Findings highlight the need to improve mental health for parents of children with CMC.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".