Prevalence and Expression of Depressive Symptomatology in Students with and without Learning Disabilities
Bibliographic record
Abstract
The present study compared girls and boys with and without learning disabilities (LD) on mean reports of depressive symptoms, prevalence of depression and type of depressive symptoms reported. One hundred children with LD (46 girls, 54 boys) ana 104 children without LD (50 girls, 54 boys) were compared on the Children's Depression Inventory's (Kovacs, 1992) overall score, percent meeting the cutoff for depression (19) and subscale factor scores indicating symptom patterns. Results revealed that (a) mean level of depressive symptoms between students with and without LD did not differ but prevalence of depression was marginally different; (b) girls with LD reported higher mean levels of depressive symptoms and higher prevalence of depression than girls without LD, whereas there was no difference in mean levels of depressive symptoms or prevalence of depression for boys with or without LD; (c) students with LD reported more symptoms of ineffectiveness; (d) girls reported more negative mood and less interpersonal problems than boys; and (e) girls with LD reported more symptoms indicative of a loss of pleasure, negative self-esteem and interpersonal problems relative to their peers without LD, while boys with or without LD did not differ in their symptom type reports. Implications and limitations of the results are discussed with reference to previous research and directions for future investigation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".