Detecting Data Falsification by Front-line Development Workers: A Case Study of Vaccination in Pakistan

要旨

Front-line workers in global development are often responsible for data collection and record-keeping about their own work. The authenticity of such data and the role of mid-level supervisors, however, remains understudied. We report on the case of immunization in Pakistan, where, through interviews with 30 mid-level vaccination managers in Punjab district, we find that data falsification by vaccinators is common, though not necessarily rampant. Because of an intricate protocol for record-keeping, supervisors can detect data falsification, and we find they have devised an array of methods, broadly classifiable into four types: triangulation, supplementary data collection, anomaly detection, and interrogation. We also find that the strategies that supervisors use to detect falsification seem linked to their style of management, with authoritarian supervisors preferring supplementary data collection and spot checks, while supportive supervisors use triangulation. Our findings lead to recommendations for designing technologies intended to monitor and manage front-line data.

著者
Amna Batool
University of Michigan, Ann Arbor, Michigan, United States
Kentaro Toyama
University of Michigan, Ann Arbor, Michigan, United States
Tiffany Veinot
University of Michigan, Ann Arbor, Michigan, United States
Beenish Fatima
Information Technology University, Lahore, Pakistan
Mustafa Naseem
University of Michigan Ann Arbor, Ann Arbor, Michigan, United States
DOI

10.1145/3411764.3445630

論文URL

https://doi.org/10.1145/3411764.3445630

動画

会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Justice, Wellbeing, and Health

[A] Paper Room 13, 2021-05-12 17:00:00~2021-05-12 19:00:00 / [B] Paper Room 13, 2021-05-13 01:00:00~2021-05-13 03:00:00 / [C] Paper Room 13, 2021-05-13 09:00:00~2021-05-13 11:00:00
Paper Room 13
14 件の発表
2021-05-12 17:00:00
2021-05-12 19:00:00
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