Development of a brief screening method for identification of depression in older adults in Sub-Saharan Africa
Bibliographic record
Abstract
OBJECTIVES: To develop a brief, culturally appropriate screening tool for identifying late life depression (LLD), for use by non-specialist clinicians in primary and out-patient care settings in sub-Saharan Africa (SSA). BACKGROUND: Depressive disorders are a leading contributor to the global health burden. LLD is common and cases will increase as populations' age, particularly in low- and middle-income countries (LMICs), such as those in SSA. A chronic mental health workforce shortage and the absence of culturally adapted LLD screening tools to aid non-specialist clinicians have contributed to a significant diagnostic gap. DESIGN: A systematic random sample of older people attending general medical clinics were interviewed using a 30-item LLD questionnaire, developed utilizing a Delphi consensus analysis of items from the Geriatric Depression Scale, Patient Health Questionnaire-2 and questions developed from a study of lay conceptualisations of depression in Tanzania. The items were assessed for validity against blinded DSM 5 diagnosis of depression by a research doctor. Factor and item analysis were then used to refine the questionnaire. RESULTS: The 12-item Maddison Old-age Scale for Identifying Depression (MOSHI-D) was developed. It has good internal consistency (Cronbach's α = 0.820) and construct and criterion validity (AUROC = 0.880). CONCLUSIONS: On initial evaluation, the MOSHI-D showed good internal validity. It should be easy for non-specialists to administer. External validation and further refinement will be conducted. A culturally-appropriate LLD screen may improve mental health care integration into existing healthcare settings within SSA and facilitate greater patient access to care, in accordance with current WHO strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".