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Record W4377987024 · doi:10.2196/43972

Gender-Specific Association Between Perceived Stigma Toward Tuberculosis and Acceptance of Preventive Treatment Among College Students With Latent Tuberculosis Infection: Cross-Sectional Analysis

2023· article· en· W4377987024 on OpenAlexvenueno aff
Yemin Yuan, Jin Jin, Xiuli Bi, Hong Geng, Shixue Li, Chengchao Zhou

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaShandong University
KeywordsMedicineStigma (botany)Latent tuberculosisTuberculosisLogistic regressionCross-sectional studySocial stigmaFamily medicineClinical psychologyAssociation (psychology)PsychologyPsychiatryMycobacterium tuberculosisInternal medicineHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

BACKGROUND: With the increasing enrollment scale of colleges, the number of students on campus has risen sharply in China. The number of students with tuberculosis (TB) and rifampicin-resistant TB in colleges has increased significantly. Preventive treatment of latent tuberculosis infection (LTBI) is an important means for TB prevention and control in colleges. At present, the acceptance of LTBI treatment among college students remains unclear. In addition, evidence shows stigma may be one of the key factors affecting acceptance of LTBI treatment. To date, there is little direct evidence on the gender-specific association between perceived stigma toward TB and acceptance of LTBI treatment among college students. OBJECTIVE: This study aimed to describe the acceptance of LTBI treatment among college students in an eastern province of China to explore the association between perceived stigma toward TB and acceptance of LTBI treatment and to examine the moderating effect of gender on the association. METHODS: Data were derived from the project on the evaluation of LTBI treatment and its effectiveness among college students in Shandong, China. In total, 1547 college students were included in the analysis. We considered covariates at the individual and family levels. Multilevel mixed-effects logistic regression was used to examine the moderating role of gender and also explore the association between perceived stigma toward TB and acceptance of LTBI treatment. RESULTS: The acceptance rate of LTBI treatment among the diagnosed college students was 46.7% (n=723). The proportion of female students (n=361, 51.5%) accepting LTBI treatment was higher than that of male students (n=362, 42.8%; P=.001). There was an interaction between perceived stigma toward TB and gender (OR 0.93, 95% CI 0.87-1.00; P=.06). Among college students with LTBI, perceived stigma toward TB was positively associated with acceptance of preventive treatment (OR 1.03, 95% CI 1.00-1.08, P=.05). Perceived stigma toward TB was positively associated with accepting LTBI treatment only among male students (OR 1.07, 95% CI 1.02-1.12; P=.005). CONCLUSIONS: The acceptance rate of preventive treatment among college students with LTBI was low. Contrary to our expectations, perceived stigma toward TB was positively associated with acceptance of preventive treatment. Gender moderated this association; high perceived stigma toward TB was associated with acceptance of preventive treatment only in male gender. Gender-specific strategies are effective in improving the acceptability of LTBI treatment in colleges.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.074
GPT teacher head0.384
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

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