Vulnerability analysis of Haitian adolescent girls before pregnancy: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: This article aims to analyze the vulnerabilities experienced by Haitian adolescent girls before their pregnancy. METHODS: A qualitative research design was developed from Dewey's social survey. From October 2020 to January 2021, semi-structured interviews were conducted with 33 pregnant adolescents living in Haiti's North and North-East departments. Thematic data analysis was performed using the qualitative data analysis software QDA miner, 6.0.5. RESULTS: The adolescent girls interviewed were between 14 and 19. The study showed that adolescent girls experienced economic and social hardship, gender issues, and barriers to contraceptive use before pregnancy. These girls have experienced restrictive conditions that make them vulnerable to risky sexual practices and unwanted pregnancy. CONCLUSIONS: The results have indicated that Haitian adolescent girls' vulnerabilities before their pregnancy result from economic, social, and cultural injustices to which they are exposed from early childhood. These adolescent girls are also highly vulnerable to sexual exploitation and rape, as well as pregnancy. It is essential to address these issues when implementing programs aimed at improving the living conditions of adolescents in Haiti, including the prevention of early and unwanted pregnancy.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".