Estimation of prevalence of mental health problems in 8-10 years old georgian children by using the strengths and difficulties questionnaire*
Bibliographic record
Abstract
Introduction Mental health problems are frequent among children and seems to predict mental disorders in adulthood. Objectives The study aimed whether the gender differences affects the Strengths and Difficulties Questionnaire (SDQ) assessments performed by parents and teachers in Republic of Georgia. Methods In 2019 a cross sectional survey in four main cities of Georgia was conducted; Totally 8-10 y old 16654 children from 211 public schools were included. SDQ completed by parents and school teachers was used to determine emotional and behavioral problems among Georgian children. Results 16654 (74%) parents out of 22553 were agreed to participate in the study. 1565 (9.39%) children were rated screen positive in top five percentile by either parent or teacher or both of them. Cut-off scores for 99-95 percentiles (top 1-5%) was defined. Boys were more likely to be rated screen positive than girls, especially by teachers: parents rated screen positive 7.5% of females, teachers - 7.2%, while males 9.4% and 11.5% respectively. Pairwise correlation coefficients (0.53) revealed moderate correlations according to p-values (< 0.05) between scores and all correlations were statistically significant. Conclusions The study defined the cut-off scores of SDQ for 8-10 y old children and a gender differences in prevalence of mental health problems in Georgia. SDQ could be used in primary healthcare and school settings to identify children with special needs. This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNSFG), grant - FR-18-304. Disclosure No significant relationships.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".