The problem of diabetics dropping out of treatment at the Abidjan Anti-diabetic Center (CADA)
Bibliographic record
Abstract
This article aims to analyze the social dynamics that contribute to the therapeutic drop-out observed in 43% of diabetics followed at the Center Anti-Diabétique d'Abidjan (CADA) over the period 2020-2023. The approach that structures this research work, which took place from February 12 to 23, 2024, is based on a mixed approach (qualitative and quantitative). For the collection of survey data, the study mobilized the technique by semi-directed interview and questionnaire and was based on reasoned choice sampling. The main target population is diabetic patients who were followed in Canada and who, at some point in their journey, disappeared from the ranks of patients who come to medical appointments. Health professionals and leaders of national diabetic associations were also interviewed to obtain additional information beyond that provided by diabetics. The main results of this research show that the socio-demographic profile including age, education level, purchasing power of the diabetics concerned, their perception of anti-diabetic treatment related to socio-cultural burdens and induced restrictions as well as the disintegration of social support associated with therapeutic pluralism that is available to them lead to a shift away from formal medical follow-up in Cada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".