Predictors of unrecognised comorbid depression in patients with schizophrenia at Amanuel mental specialized hospital, Ethiopia: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The occurrence of depression in patients with schizophrenia (PWS) increases the risk of relapse, frequency and duration of hospitalisation, and decreases social and occupational functioning. OBJECTIVE: This study aimed to assess prevalence of unrecognised comorbid depression and its determinants in PWS. METHOD: A cross-sectional study was conducted from 1 to 30 March 2019 at Amanuel mental specialized hospital among 300 PWS. The 9-item Calgary Depression Scale for Schizophrenia was used to assess comorbid depression. Logistic regression was used to determine the association between outcome and explanatory variables. Statistical significance was declared at p value <0.05 with 95% CI. RESULTS: The prevalence of unrecognised comorbid depression was found to be 30.3%. Living alone (adjusted OR (AOR)=3.49, 95% CI=0.45 to 8.36), having poor (AOR=4.43, 95% CI=1.45 to 13.58) and moderate (AOR=4.45, 95% CI=1.30 to 15.22) social support, non-adherence to medication (AOR=3.82, 95% CI=1.70 to 8.55), presenting with current negative symptoms such as asocialia (AOR=4.33, 95% CI=1.98 to 9.45) and loss of personal motivation (AOR=3.46, 95% CI=1.53 to 7.84), and having suicidal behaviour (AOR=6.83, 95% CI=3.24 to 14.41) were the significant predictors of comorbid depression in PWS. CONCLUSION: This study revealed considerably a high prevalence of unrecognised comorbid depression among PWS. Therefore, clinicians consider timely screening and treating of comorbid depression in PWS.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".