Unmet Needs of Children With Special Health Care Needs When Transitioning to Adult Services
Bibliographic record
Abstract
Health care transition planning could help decrease health care disparities among those who identify as an individual on the autism spectrum; however, less than a quarter of the adolescent population living with autism spectrum disorder receive transition planning. Decreasing health care disparities could lead to an increase in health care quality and longevity. The purpose of this study was to analyze if there was a relationship between (a) the number of physician visits, (b) if the physician treats only children, (c) if the physician talks about the child seeing physicians who treat adults, (d) if the physician worked with the guardian to create a plan that identifies specific health goals or needs, and if (a) the physician helped the child develop skills to manage their health and (b) if the physician worked with the child to understand health care changes for both children with and without special health care needs. Secondary data from 5,638 parental respondents of children aged 17 years old to the 2017 National Survey of Children’s Health (NSCH) were used to conduct this quantitative analysis employing chi square tests guided by the Donabedian structure/process/outcome model. The findings support the hypothesis that a statistically significant relationship does not exist between the dependent variables and covariates. Future research could incorporate physician response data or racial and socioeconomic variables to find further significance. The findings of the study can positively influence social change by allowing health care administrators to implement initiatives within their organizations to further the improvement of transitional planning for adolescents.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".