Screening for parent and child ADHD in urban pediatric primary care: pilot implementation and stakeholder perspectives
Bibliographic record
Abstract
Abstract Background ADHD commonly co-occurs in children and parents. When ADHD is untreated in parents, it contributes to negative child developmental and treatment outcomes. Screening for parent and child ADHD co-occurrence in pediatric primary care may be an effective strategy for early identification and treatment. There is no data on whether this screening model can be implemented successfully and there exists limited guidance on how to effectively approach parents about their own ADHD in pediatric settings. Even greater sensitivity may be required when engaging with families living in urban, low SES communities due to systemic inequities, mistrust, and stigma. Methods The current pilot study described the first 6 months of implementation of a parent and child ADHD screening protocol in urban pediatric primary care clinics serving a large population of families insured through Medicaid. Parents and children were screened for ADHD symptoms at annual well-child visits in pediatric primary care clinics as part of standard behavioral health screening. Independent stakeholder group meetings were held to gather feedback on factors influencing the implementation of the screening and treatment strategies. Mixed methods were used to examine initial screening completion rates and stakeholder perspectives (i.e., parents, primary care office staff, pediatricians, and behavioral health providers) on challenges of implementing the screening protocol within urban pediatric primary care. Results Screening completion rates were low (19.28%) during the initial 6-month implementation period. Thematic analysis of stakeholder meetings provided elaboration on the low screening completion rates. Identified themes included: 1) divergence between provider enthusiasm and parent hesitation; 2) parent preference versus logistic reality of providers; 3) centering the experiences of people with marginalized identities; and 4) sensitivity when discussing parent mental health and medication. Conclusions Findings highlight the importance of developing flexible approaches to screening parent and child ADHD in urban pediatric health settings and emphasize the importance of cultural sensitivity when working with marginalized and under-resourced families. Trial registration NCT04240756 (27/01/2020).
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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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".