Does chronicity necessarily lead to patient policy participation? Diabetes & HIV/AIDS cases in Mali
Bibliographic record
Abstract
Background According to the Ottawa charter, individuals and communities are key partners for questions related to their health. Patients’ participation is particularly stressed in strategies targeting chronic diseases. We analyse which factors shape patients’ participation in policy-making about diabetes and HIV/AIDS in Mali, West Africa. In the context of Southern countries, most studies concern HIV/AIDS, while few exist on non-communicable diseases. As the burden of chronic diseases is rapidly increasing in the South, we aim to improve the implementation of patients’ participation. Methods We collected our data between 2008 and 2014 at two-year intervals by means of semi-structured interviews, non-participant observations and a literature search. In Bamako, we met 79 representatives of public authorities, patient associations, NGOs, caregivers and donors who fight against diabetes and HIV/AIDS. From an historical approach, we retraced patients’ mobilisation since the 1980s. Results The place and roles given to patients, individually and collectively, vary over time according to several intertwining factors. Among them: the political context, as patients mobilise in reaction to state commitment; the medical history of the disease, which shapes the relations between ordinary patients and experts; cultural and social elements, particularly how decision-makers, caregivers and donors view both the disease and patients; donors’ impact on patients’ capacity to mobilise and on the public space architecture. Conclusions Thirty years later, we are still far from the Ottawa spirit. Chronicity is not a sufficient condition to build patient’s political legitimacy, and we question the image of an active chronic patient. The cases of diabetes and HIV/AIDS in Bamako reveal how chronic patients are in fact intermittent partners for policy-makers. Patient’s participation is a social construction linked to many factors which need to be considered to improve health democracy. Key messages: This is the first comparative study on diabetes and HIV/AIDS in Bamako, Mali and among the few analyses targeting patients’ participation in policy decision-making in Southern countries Patient’s participation is a social construction linked to many factors. Chronicity is not a sufficient condition to build patient’s legitimacy, and we question the image of an active patient
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".