Evidence map›Paper›PMID 34836912›Full record

ArticleBMJ global health2021

Gender-related data missingness, imbalance and bias in global health surveys.

Ann M Weber, Ribhav Gupta, Safa Abdalla, Beniamino Cislaghi, Valerie Meausoone, Gary L Darmstadt

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ann M WeberSchool of Public Health, University of Nevada Reno, Reno, Nevada, USA annweber@unr.edu.ORCID 0000-0001-8130-5858
Ribhav GuptaDepartment of Medicine, University of Minnesota School of Medicine, Minneapolis, Minnesota, USA.
Safa AbdallaGlobal Center for Gender Equality, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.
Beniamino CislaghiDepartment of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.
Valerie MeausooneStanford Research Computing Center, Stanford University, Stanford, California, USA.
Gary L DarmstadtGlobal Center for Gender Equality, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.ORCID 0000-0002-7522-5824

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global surveys have built-in gender-related biases associated with data missingness across the gender dimensions of people's lives, imbalanced or incomplete representation of population groups, and biased ways in which gender information is elicited and used. While increasing focus is being placed on the integration of sex-disaggregated statistics into national programmes and on understanding effects of gender-based disparities on the health of all people, the data necessary for elucidating underlying causes of gender disparities and designing effective intervention programmes continue to be lacking. Approaches exist, however, that can reasonably address some shortcomings, such as separating questions of gender identification from biological sex. Qualitative research can elucidate ways to rephrase questions and translate gendered terms to avoid perpetuating historical gender biases and prompting biased responses. Non-health disciplines may offer lessons in collecting gender-related data. Ultimately, multidisciplinary global collaborations are needed to advance this evolving field and to set standards for how we measure gender in all its forms.

Indexed as

Global HealthSexismBiasHealth SurveysHumansSurveys and Questionnairesepidemiologyhealth policypublic health

Identifiers

PMID34836912
PMCPMC8628344

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.