Tande nou gwonde! (Hear us roar!)- Youth perspectives of maternal near-misses: Protocol for a photovoice study of young childbearing people’s perspectives of maternal near-misses in northwest Haiti
Bibliographic record
Abstract
INTRODUCTION: Globally, a shift is occurring to recognize the importance of young peoples' health and well-being, their unique health challenges, and the potential they hold as key drivers of change in their communities. In Haiti, one of the four leading causes of death for those 20-24 years old is pregnancy, childbirth, and the weeks after birth or at the end of a pregnancy. Important gaps remain in existing knowledge about youth perspectives of maternal health and well-being within their communities. Youth with lived experiences of maternal near-misses are well-positioned to contribute to the understanding of maternal health in their communities and their potential role in bringing about change. OBJECTIVES: To explore and understand youth perspectives of maternal near-miss experiences that occurred in a local healthcare facility or at home in rural Haiti. METHODS: We will conduct a qualitative, community-based participatory research study regarding maternal near-miss experiences to understand current challenges and identify solutions to improve community maternal health, specifically focused on youth maternal health. We will use Photovoice to seek an understanding of the lived experiences of youth maternal near-miss survivors. Participants will be from La Pointe, a Haitian community served by their local healthcare facility. We will undertake purposeful sampling to recruit approximately 20 female youth, aged 15-24 years. Data will be generated through photos, individual interviews and small group discussions (grouped by setting of near-miss experience). Data generation and analysis are expected to occur over a three-month period. ETHICS AND DISSEMINATION: Ethics approval will be sought from Centre Médical Béraca in La Pointe, Haiti, and from the Hamilton Integrated Research Ethics Board in Hamilton ON, Canada. We will involve community stakeholders, especially youth, in developing dissemination and knowledge mobilisation strategies. Our findings will be disseminated as an open access publication, be presented publicly, at conferences, and defended as part of a doctoral thesis.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.095 | 0.019 |
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".