Evidence map›Paper›PMID 38420140›Full record

ArticleStigma and health2024

A Systematic Review and Psychometric Appraisal of Instruments Measuring Tuberculosis Stigma in Sub-Saharan Africa.

Alanna J Bergman, Katherine McNabb, Jason E Farley

Open access · greenAbstract read
In one paragraph

Article in Stigma and health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.4field-weighted citation impact, top 37% of its field
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

10 citing papers in PubMed, 12 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Alanna J BergmanJohns Hopkins University School of Nursing, Center for Infectious Disease and Nursing Innovation.
Katherine McNabbJohns Hopkins University School of Nursing, Center for Infectious Disease and Nursing Innovation.
Jason E FarleyJohns Hopkins University School of Nursing, Center for Infectious Disease and Nursing Innovation.
Johns Hopkins University · US

Funding

Understanding and Predicting Loss to Follow-up from Multi-Drug Resistant Tuberculosis Treatment in the Setting of High-HIV BurdenF30AI165167 · NIAID · JOHNS HOPKINS UNIVERSITY · PI MCNABB, KATHERINE C · 2021 to 2023
$149k
Exploring the Impact of Illness Identity on Intersectional Stigma Through the Care ContinuumF31NR020588 · NINR · JOHNS HOPKINS UNIVERSITY · PI BERGMAN, ALANNA J · 2022 to 2023
$88k
NIAID NIH HHS F30 AI165167NINR NIH HHS F31 NR020588
6 · The paper itself

Abstract

Tuberculosis (TB) stigma is one barrier to TB testing, treatment uptake and treatment completion. Therefore, stigma measurement must be approached through rigorous scientific methodology in order to accurately and reliably estimate the impact of TB stigma on treatment outcomes. The aim of this systematic review is to evaluate the methods and instruments used to measure TB stigma and interrogate strategies used to culturally validate measures of TB stigma in global research. Two reviewers used the PRISMA method to extract and analyze the existing body of literature on TB stigma in Sub-Saharan Africa. A thorough search was performed using three data bases generating 2,302 independent studies. After systematic screening, this review includes 28 studies. Of those studies, 13 used a psychometrically validated instrument while 15 used informal questionnaires or proxy variables to measure stigma. Psychometric appraisal was limited due to the number of studies that measured stigma using unvalidated questionnaires or proxy variables. The

Indexed as

measurementpsychometricstigmaTB/HIVTuberculosisvalidity

Identifiers

PMID38420140
PMCPMC10901500
OpenAlexW3173587095

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.