Cited by 78Qualitative Research Methods and EthicsCommunity Health and DevelopmentFeminism, Gender, and Intersectionality

Engaging Solidarity in Data Collection Practices for Community Health

Ismail, Azra, Kumar, Neha·2018·Proceedings of the ACM on Human-Computer Interaction

Abstract

Researchers and practitioners engaged in global health are increasingly turning to data-driven approaches. However, the institutionalized roles and responsibilities---as well as motivations and workflows---of those involved in data collection are not well understood. To attain a deeper understanding of the practices of frontline health workers engaged in data collection, we conducted fieldwork in underserved contexts of Delhi, India. In particular, we interviewed these workers and observed their interactions with 200 households. Our analysis takes a postcolonial feminist perspective to examine how these frontline health workers navigate the multiple demands placed on them by their families, society, local residents, and health organizations. We also discuss how they practice feminist solidarity, and distill lessons for improved data collection across global health initiatives.

Integrity signals

Registry

Crossref · 10.1145/3274345

Cites

67 of 67 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Traditional societies often value restorative justice, which focuses on repairing harms, rather than fairness.

    “Traditional societies often value restorative justice, which emphasises repairing harms [44], rather than fairness, e.g., contemporary Australian Aboriginal leaders emphasise reconciliation rather than fairness in their political goals [94].”

    From Re-imagining Algorithmic Fairness in India and Beyond (2021) · cites this paper as [94]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Ismail, A., & Kumar, N.
Affiliations
Georgia Institute of Technology (US)
Venue
Proceedings of the ACM on Human-Computer Interaction
Published
2018-11
Volume
2
Issue
CSCW
Pages
1-24
ISSN
2573-0142 (ISSN-L)
OpenAlex
W2898898463
Cited by
78
References
67
Citations by year
2025: 12 · 2024: 9 · 2023: 12 · 2022: 14 · 2021: 13 · 2020: 8 · 2019: 10
FWCI
17.64
Type
journal article
Language
en
Open access
closed
Locations
Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Qualitative Research Methods and Ethics
  • Health Sciences › Health Professions › General Health Professions › Community Health and Development
  • Social Sciences › Sociology and Political Science › Feminism, Gender, and Intersectionality
Keywords
data collection practices, frontline health workers, feminist solidarity, postcolonial feminism, community health, global health, underserved populations, Delhi

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review