Exploring the feasibility of establishing a core set of sexual, reproductive, maternal, newborn, child and adolescent health indicators in humanitarian settings: a multimethods, multicountry qualitative study protocol
Bibliographic record
Abstract
INTRODUCTION: In 2019, over 70 million people were forcibly displaced worldwide. Women and girls comprise nearly half of this population and are at heightened risk of negative sexual and reproductive health outcomes. With the collapse of health systems, reduced resources and increased vulnerabilities from displacement, there is a need to strengthen current practices and ensure the delivery of comprehensive sexual, reproductive, maternal, newborn, child and adolescent health (SRMNCAH) services. Recognising the need for consistency in data collection, analysis and use, the WHO developed a list of core SRMNCAH monitoring and evaluation indicators for services and outcomes in humanitarian settings. This research will explore the feasibility of collecting this core set of SRMNCAH indicators in displacement contexts. METHODS AND ANALYSIS: We will undertake a multimethods qualitative study in seven humanitarian settings: Afghanistan, Albania, Bangladesh, Cameroon, the Democratic Republic of the Congo, Iraq and Jordan. We selected sites that reflect diversity in geographic region, sociocultural characteristics, primary location(s) of displaced persons and nature and phase of the crisis. Our study consists of four components: key informant interviews, facility assessments, observational sessions at select facilities and focus group discussions with front-line healthcare personnel. We will analyse our data using descriptive statistics and for content and themes. We will begin by analysing data from each setting separately and will then combine these data to explore concordant and discordant results, triangulate findings and develop global recommendations. ETHICS AND DISSEMINATION: The University of Ottawa's Research Ethics Board and the Research Project Review Panel (RP 2) of the World Health Organization-Department of Sexual and Reproductive Health as well as local IRBs of PIs' research institutions reviewed and approved this protocol. We intend to disseminate findings through workshops at the WHO country, regional and headquarter levels, as well as through local, national and international conferences, workshops, peer-reviewed publications, and reports.
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 | 0.116 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".