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 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".