Prevalence and Associated Factors of Attention Deficit Hyperactivity Disorder (ADHD) in a Rural Community, Central Thailand: A Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common behavioral problems among children around the world including Thailand. The disease affects their life, parents and community when left untreated. Most information concerning ADHD in Thailand derives from hospital based studies. The present study aimed to determine the prevalence and associated factors of ADHD in a remote rural community.METHODS: A total of 495 primary school children were screened using the SNAP-IV. Positive screening cases were then diagnosed by pediatric psychiatrists according to the DSM-V criteria. Standardized questionnaires were used to collect demographic data and associated factors. A qualitative study using focus group discussions and indepth interviews was conducted to determine knowledge and perceptions regarding ADHD among teachers and main guardians.RESULTS: The prevalence of ADHD among children was 2.2%. Univariate and multivariate analysis showed that children with ADHD were associated with both familial and individual factors including being repeatedly inattentive or hyperactive in class, suspended from school, and changing school, a history of bullying and main guardians were not parents. Qualitative data showed that both main guardians and teachers had inadequate knowledge and misperceptions regarding children with ADHD. The local health care system could not detect this problem so the children with ADHD were not properly treated.CONCLUSION: Our data emphasized that ADHD was a problem in this remote rural community. Screening tests and referral systems for ADHD should be provided for rural communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".