Prevalence of Childhood Depression: The Effects of Teacher-Students Relationship as Predictor Factors to Depressive Symptoms
Bibliographic record
Abstract
The aims of this study are to determine the prevalence of depression among children, and scrutinize the teacher-student relationship as predictor factor to depressive symptoms according to the children's perception. The Jordanian translation of Children's Depression Inventory (CDI) was used in this study. The study was carried out among children in private and state schools within the age range of (7-13) in Amman city. The sample on which the study tools were applied consisted of 705 children. The results of this study indicate that the prevalence of moderate depression was the lowest (3.8%) at the age of 8 and the highest (5.8%) at the age of 11. The prevalence of severe depression was lowest (2.3%) at the age of 9 and highest (6.8%) at the age of 13. The results also show that there is no single cause of depression and it is difficult to separate the different causes. All the children with severe depression discuss how the teacher-student relationship as predictor factors to depressive symptoms. From the analysis of the results, specific reasons to teacher-student relationships that cause depressive symptoms are categorised into two basic factors these are: The teacher-student interaction, and peer influence.
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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".