Adapter les pratiques médicales au terrain : maternité et VIH en Guyane et à Saint-Martin
Bibliographic record
Abstract
Because of its high prevalence, HIV in pregnancy is a major public health issue in French Guyana and Saint Martin, particularly since the risk of transmission to the child can be significantly reduced through pharmacological treatment. Most of the HIV-infected women in these areas are immigrants living in highly precarious circumstances. This study examines the capacity of the healthcare system to adapt to the specific social characteristics of overseas regions, focusing in particular on perceptions of the risks associated with pregnancy among HIV-infected women and the social inequalities affecting adherence to HIV treatment. Semi-structured interviews were conducted in Cayenne, Saint-Laurent du Maroni and Saint-Martin with 19 HIV-infected women and 54 social and health care professionals. Observations (medical consultations, therapeutic education consultations, discussion groups, medical meetings) were also conducted to complete the data set. The results show that professionals tend to use the most significant concern expressed by HIV-infected women - i.e. the risk of transmitting their infection to their child - as an opportunity to promote the active involvement of patients in their own care and the health care of their children by encouraging them to adhere to their treatment. The study found that professionals seek to lessen the impact of social inequalities on patient adherence to the treatment in a context of social stigmatization linked to the particular status of their patients as HIV-infected women, undocumented migrants, and ethnic minority members. The example of HIV in pregnancy illustrates the capacity of the healthcare system to reduce the impact of social inequalities on health and highlights the significant negative impact that a reduced commitment to this issue would have.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".