Expression of Depressive Symptoms in a Nonclinical Brazilian Adolescent Sample
Bibliographic record
Abstract
OBJECTIVE: This study aimed to detect the prevalence of depressive symptomatology and its expression in a nonclinical Brazilian adolescent student sample. METHOD: A sample of students from private and public schools (n = 1555, aged 13 to 17 years) answered the Beck Depression Inventory (BDI). We performed factor analysis of the BDI as an indicator of the expression of depressive symptomatology. The following cut-off scores defined nonclinical subgroups: "nondepressed," BDI < 15; "dysphoria," BDI 16 to 20; and "depressed," BDI > 20. We used discriminant analysis to test whether these subgroups could be separated by the depression-specific and nonspecific items. RESULTS: The point prevalence of depression was 7.6%, according to the BDI cut-off of 20. Girls had higher scores than boys in several items. Scores increased with age. Students from public schools had higher scores than did private school students. Factor analysis showed 2 common factors for the total sample and for each sex: the cognitive affective dimension and the somatic nonspecific dimension. In the adolescents showing clinical depression, items related to self-depreciation, sense of failure, guilty feelings, self-dislike, suicidal wishes, and distortion of body image were common components of BDI factors. Discriminant analysis showed that the BDI highly discriminates depressive symptomatology in adolescent students and also measures specific aspects of depression. CONCLUSIONS: The BDI is useful as a measure of specific aspects of depression in nonclinical adolescent samples; it was able to detect depression in approximately 7% of the surveyed population. The expression of depressive symptoms in a Brazilian adolescent population is compatible with international studies in this age group. Detecting depressive symptoms in a school population is a critical preventive strategy; to avoid damage to the learning process, it should be followed with further referral to treatment when needed.
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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 | 0.003 |
| 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) | 0.001 | 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".