Review: Validation of the Child Depression Screening Tool in three African settings: Rwanda, Senegal and South Africa — R1/PR7
Bibliographic record
Abstract
The unavailability of reliable, easy-to-use depression screening tools adapted for Sub-Saharan African children is a significant barrier to the treatment of childhood depression. We thus adapted the Child Depression Screening Tool (CDST) to the South African (SA), Senegalese (S) and Rwandan (R) contexts, as a tool to screen for depression in children suffering from chronic illnesses, trauma and difficulties related to COVID-19, family and community hardships. A DSM-5-based diagnostic interview and the CDST screening measure were administered to 1,001 participants aged between 7 and 16 years. The prevalence of depression ranged between 9.5 and 16.8%. It was more prevalent in youth with chronic illness and those exposed to adverse life events. Older age (R and SA), female sex (S), dislike of school (R and SA) and cannabis use (SA) were also associated with worse depression. Receiver operating characteristic analysis showed satisfactory performance (79-89%) and that sensitivity and specificity were optimized at a CDST cut-point of 5.0. The CDST is a valid tool to screen for depression in the settings assessed. If found to be suitable in other countries and settings, it may offer a clinically sound, sustainable path towards the identification of child depression in Africa.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".