Factors associated with the prevalence of depression among people with oculocutaneous albinism in Jinja,Uganda.A cross sectional study
Bibliographic record
Abstract
Abstract Background Depression is among the common psychiatric disorders with high prevalence in the general population.This prevalence is higher in vulnerable populations including people living with albinism. Despite the fact that several aspects linked with it have been found among people with oculocutaneous albinism in the Busoga region, limited information is available regarding prevalence of depression and its associated factors in the study area.The main objective of the present study was to determine the factors associated with the prevalence of depression among people with oculocutaneous albinism in Jinja. Methods A cross-sectional design was used to capture data from a study sample size of 384 adults living with oculocutaneous albinism who were involved in completion of the screening tests for depression Hopkins Symptom Checklist-25(HSCL-25).The summation of scores for depression were averaged and the probable depression determined for each participant using a cut-off of 1.75. Logistic regression analyses were used to examine associations between depression outcomes, socio-demographic and psychomedical factors. Results The analyses revealed that the prevalence of depression among people with oculocutaneous albinism in Jinjacity stands at 65.4%. Depression was significantly associated with age (AOR = 1.059, 95% CI = 1.020–1.100, P = 0.003), lack of family support (AOR = 0.505, 95% CI = 0.286–0.892, P = 0.019), history of diabetes mellitus (AOR = 12.030, 95% CI = 1.117–12.961, P = 0.040), marital status by being married(AOR = 0.505, 95% CI = 0.286–0.892, P = 0.019) and taking chronically medication (AOR = 6.583, 95% CI = 1.618–26.782, P = 0.008). Conclusions These findings show that the estimated prevalence of depression among people with oculocutaneous albinism in the study area is high and worrying. Age, marital status, lack of family support, history of diabetes mellitus, and taking chronically medication are important risk factors associated with the prevalence of depressive disorders. Strategies targeting early interventions are needed in order to reduce risk factors of the disease and improve the quality of life of people with oculocutaneous albinism in Jinja.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".