Longitudinal depressive and anxiety symptoms of adult injury patients in Kenya and their risk factors
Bibliographic record
Abstract
Background: Injuries account for a significant proportion of the health and economic burden for populations in low- and middle-income countries. However, little is known about psychological distress trajectories amongst injury survivors in low- and middle-income countries.Methods: Adult injury patients (n = 644) admitted to Kenyatta National Hospital in Nairobi, Kenya, were enrolled and interviewed in the hospital, and at 1, 2–3, and 4–7 months after hospital discharge through phone to assess depressive and anxiety symptoms and level of disability. Growth mixture modeling was applied to identify latent trajectories of depressive and anxiety symptoms.Results: Elevated depressive and moderate-level anxiety symptoms (13%) and low depressive and anxiety symptoms (87%) trajectories were found between hospitalization and up to seven months after hospital discharge. Being female, prior trauma experience, longer hospitalization, worse self-rated health status while in the hospital, and lack of monetary assistance during hospitalization were associated with the elevated symptoms trajectory. The higher symptoms trajectory associated with higher disability levels after hospital discharge and significantly lower proportion of resuming daily activities and work.Conclusion: The persistence of elevated depressive symptoms and associated reduced functioning several months after physical injury underscores the importance of identifying populations at risk for preventive and early interventions.Implications for RehabilitationHealth providers following up with injury survivors should screen for depressive and anxiety symptomsSpecial attention to women and people with a potential traumatic exposure historyIncorporation of evidence-based culturally adapted psychosocial interventions in rehabilitation and outpatient clinics
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".