Perspectives of Rural Primary Care Clinicians on Pediatric Attention-Deficit/Hyperactivity Disorder Care
Bibliographic record
Abstract
OBJECTIVE: Despite efficacious treatments, evidence-based guidelines, and increased availability of integrated behavioral health care, youth coping with attention-deficit/hyperactivity disorder (ADHD) receive suboptimal care. More research is needed to understand and address care gaps, particularly within rural health systems that face unique challenges. We conducted a qualitative study within a predominantly rural health system with a pediatric-integrated behavioral health care program to address research gaps and prepare for quality improvement initiatives, including primary care clinician (PCC) trainings and clinical decision support tools in the electronic health record (EHR). METHOD: Semistructured interviews were conducted with 26 PCCs representing clinics within the health system. Interview guides were based on the Practical Robust Implementation and Sustainability Model to elicit PCC views regarding determinants of current practices and suggestions to guide quality improvement efforts. We used thematic analysis to identify patterns of responding that were common across participants. RESULTS: PCCs identified several internal and external contextual factors as determinants of current practices. Of note, PCCs recommended increased access to continuing education trainings held in clinic over lunch and delivered in less than 30 minutes. Suggested improvements to the EHR included incorporating parent and teacher versions of the Vanderbilt Rating Scale into the EHR, documentation templates aligned with evidence-based guidelines, and alerts and suggestions to aid medication management during appointments. CONCLUSION: Future research to identify implementation strategies to help rural PCCs adopt innovations are needed given the increased responsibility for managing ADHD care and intractable gaps in access to behavioral health care in rural regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".