Avoidable workload of care for patients living with HIV infection in Abidjan, Côte d’Ivoire: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: People living with HIV infection (PLWHIV) in Sub-Saharan Africa cope with an increasing workload of care (doctor visits, lab tests, medication management, refills, etc.) in a context of poor health service organization. We aimed to describe the workload of care for PLWHIV in Sub-Saharan Africa and assess to what extent simple adjustments in care organization could reduce this workload of care. METHODS: Adult PLWHIV under antiretroviral treatment for at least 1 year were recruited in three centers (two public, one private) in Abidjan, Côte d'Ivoire. Using methods inspired from sociology, we precisely described all health-related activities (HRAs) performed by patients, in 1 month, in terms of time, money and opportunity costs. Then, we assessed the theoretical avoidable workload of care if patients' visits and tests had been grouped on the same days. RESULTS: We enrolled 476 PLWHIV in the study. Patients devoted 6.7 hours (SD = 6.3), on average, in HRAs per month and spent 5% (SD = 11) of their monthly revenue, on average, on health activities. However, we found great inter-patient heterogeneity in the mixture of activities performed (managing medications; dietary recommendations; visits, tests, support groups; administrative tasks; etc.) and their time allocation, temporal dispersion and opportunity costs (personal, familial, social or professional costs). For 22% of patients, grouping activities on the same days could reduce both time and cost requirements by 20%. CONCLUSION: PLWHIV in Côte d'Ivoire have a heavy workload of care. Grouping visits and tests on the same days may be a simple and feasible way to reduce patients' investment of time and money in their care.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".