Tools for measuring sexual and reproductive health and rights (SRHR) indicators in humanitarian settings
Bibliographic record
Abstract
Abstract Background Effective measurement of all health indicators and especially SRHR is difficult in humanitarian settings. Displacement and insecurity due to conflict, natural disasters, and epidemics place women and girls at higher risk of SRHR-related morbidity and mortality and reduce the coverage of essential SRHR services. This scoping review describes the measurement tools, methods, and indicators used to measure SRHR coverage and outcome indicators in humanitarian settings in the past 15 years and presents an accessible dashboard that can be used by governments, researchers and implementing organizations to identify available SRHR measurement tools. Methods Scientific articles published between January 2004 and May 2019 were identified using Embase, Medline, PsycInfo, CINAHL, Scopus, PAIS index as well as relevant non-peer-reviewed literature available through websites of humanitarian organizations. Publications including data from low- or middle-income countries (LMICs), focused on women and/or girls living in areas impacted by a humanitarian crisis, where data was collected within five years of the crisis were included. Indicators extracted from these publications were categorized according to validated SRHR indicators recommended by the World Health Organization (WHO). Measurement tools, sampling and data collection methods, gap areas (geographical, topical and contextual), and indicators were catalogued for easy access in an interactive Tableau dashboard. Results Our search yielded 42,081 peer-reviewed publications and 2,569 non-peer-reviewed reports. After initial title and abstract screening, 385 publications met the inclusion criteria. SRHR indicators were categorized into nine domains: abortion, antenatal care, family planning, gender-based violence, HIV and sexually transmitted infections, maternal health, maternal mortality, menstrual and gynecological health, and obstetric care (delivery). A total of 65 tools and questionnaires measuring SRHR were identified, of which 25 were designed specifically for humanitarian settings. Discussion Although SRHR was measured in humanitarian settings, several gaps in measurement were identified. Abortion and gynaecological health were not consistently measured across included studies or validated WHO indicators. Toolkits and indicators identified in this review may be used to inform future SRHR data collection in humanitarian settings. However, identifying and/or developing innovative data collection methodologies should be a research priority, especially in light of the recent COVID-19 pandemic.
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 | 0.055 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.053 | 0.066 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".