Parental Challenges in Raising Preschoolers With Attention-Deficit/Hyperactivity Disorder in Mainland China’s First-Tier Cities: Qualitative Study Using Framework Analysis
Bibliographic record
Abstract
BACKGROUND: First-tier cities in mainland China present unique challenges for raising children with attention-deficit/hyperactivity disorder (ADHD) due to rapid urbanization, intense academic pressure, and distinct sociocultural dynamics. While existing research has documented ADHD parenting challenges across various contexts, limited attention has been paid to the preschool period in Chinese metropolitan settings, where early intervention is crucial yet complicated by traditional values and evolving health care systems. OBJECTIVE: This study aimed to explore the comprehensive challenges faced by parents raising preschoolers with ADHD in these metropolitan contexts. METHODS: We conducted web-based semistructured focus group interviews with 13 parents (n=12, 92% mothers and n=1, 8% fathers; aged between 32 and 44 years) of preschool children with ADHD (n=10, 77% boys and n=3, 23% girls; aged between 5 and 7 years) in first-tier cities in mainland China between March and July 2024. Participants were purposively recruited through the ADHD Mutual Support Alliance's platform if they had a preschool child with a formal ADHD diagnosis and lived in first-tier cities. Four interviews were conducted (3 focus groups with 4 participants each and 1 pilot individual interview). All interviews were moderated by a practitioner working with populations with ADHD following a semistructured interview guide developed through expert panel consultation. Interviews were audio-recorded and transcribed verbatim. Qualitative data were analyzed using framework analysis, using a theoretical framework approach, with dual-researcher coding and participant verification to ensure methodological rigor. Data were coded and organized manually using Microsoft Word, supported by NVivo software. RESULTS: Challenges emerged across six major themes: (1) individual psychological and behavioral challenges, including parental emotional burden, self-blame, and difficulties managing ADHD symptoms; (2) microsystem challenges in family and school environment challenges, particularly regarding behavioral management and teacher understanding; (3) mesosystem difficulties in family-school-hospital coordination, highlighted by recurring teacher complaints and inconsistent diagnostic standards; (4) exosystem barriers in work-family balance and health care access, including limited availability of specialized services and financial burdens; (5) macrosystem challenges from societal stigma and traditional educational values, manifesting as discrimination fears and academic pressure; and (6) chronosystem impacts from historical and social changes, notably the COVID-19 pandemic's influence and gaps in support systems compared to countries with comprehensive health care infrastructure. CONCLUSIONS: Parents face multilayered challenges in raising preschoolers with ADHD, particularly regarding access to specialized health care services, navigation of educational systems, and use of culturally adapted interventions. These challenges are intensified by the unique urban context of intense academic pressure and rapid modernization. Future initiatives should focus on enhancing health care resource accessibility, developing culturally sensitive support programs, implementing systematic educational accommodations, and promoting broader societal awareness of ADHD in mainland China. These findings underscore the necessity for comprehensive ecological interventions that address challenges across all systemic levels while considering the unique characteristics of Chinese metropolitan environments.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".