P.088 Family identified barriers to accessing services for children with attention deficits and neurodevelopmental disorders
Bibliographic record
Abstract
Background: Obtaining early intervention services is crucial for improving outcomes in children with neurodevelopmental disorders (NDDs) such as Autism or Attention Deficit/Hyperactivity Disorder. Identifying barriers in accessing services in the healthcare system is necessary to optimize the Patient and Family-Centered Care approach. Methods: Parents of children with an NDD co-occurring with attention deficits were recruited from the Neurodevelopmental Attention Clinic at the Alberta Children’s Hospital. Parents completed a semi-structured interview and the Barriers to Accessing Services (BAS) questionnaire. Results: Nine families participated representing 10 children. Interviews were evenly spread between biological mothers and fathers. All children had attention deficits; 4 children were also diagnosed with Autism and 5 with “other” neurologic conditions. The two barriers most identified by families (67%) through the BAS questionnaire were “Didn’t know where to find help” and “Steps to seek help are too overwhelming”, consistent with information obtained in the interview. Conclusions: Children with an NDD and attention deficits often have complex medical needs. Parents have identified challenges initiating, and navigating the many steps involved to secure services. We will collect information from more families to determine how services for children with complex medical needs can become more accessible.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.001 |
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".