Socio-demographic correlates of late treatment initiation in a cohort of patients starting antiretroviral treatment in Mali, West Africa
Bibliographic record
Abstract
The objective of this study was to investigate factors correlated with late treatment initiation in a cohort of patients starting treatment in Mali, West Africa, while focusing on the role of sex/gender. This study consisted of a cross-sectional analysis of baseline data from a prospective, observational cohort of patients initiating antiretroviral treatment in Mali. Patient data were analyzed with a gender perspective to examine factors correlated with late treatment initiation, defined as having a CD4 count below 100 cells/µl. Aday and Andersen's conceptual framework of access to medical care was used to classify baseline participant characteristics associated with late treatment initiation. Logistic regression was used to evaluate the modifying effect of sex/gender. Results show that 39% of patients initiated treatment late; significantly more of these were men than women. Sex/gender, marital status, and education were associated with late treatment initiation. Unmarried men and uneducated women were significantly more likely to initiate treatment late. Programs need to target unmarried men while being cognizant that uneducated women are arriving late as well.
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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 | 0.002 |
| 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 | 0.001 |
| 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".