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Record W4405326864 · doi:10.4103/pmrr.pmrr_196_24

Social Stigmatisation among Tuberculosis Patients and Community People in Bangladesh: An Exploratory Study

2024· article· en· W4405326864 on OpenAlexaff
Goutam Kumar Dutta, Md. Mostafizur Rahman, Dipika Shankar Bhattacharyya, Md. Musfikur Rahman, Palash Kumar Dey, Mosa Effat Nur, Kazi Robiul Alom

Bibliographic record

VenuePreventive Medicine Research & Reviews · 2024
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStigma (botany)Nonprobability samplingPrejudice (legal term)Thematic analysisPsychological interventionTuberculosisPsychologySocial stigmaMedicineQualitative researchClinical psychologySocial psychologyPsychiatryEnvironmental healthFamily medicineSociologyHuman immunodeficiency virus (HIV)Population

Abstract

fetched live from OpenAlex

Abstract Introduction: This study aims to investigate social perceptions and prejudice towards tuberculosis (TB) in Bangladesh and how these perceptions and prejudice contribute to retain social stigma among patients and community people. Materials and Methods: Authors employed phenomenological approach and conducted 20 in-depth interviews with TB patients and their caregivers at the hospital, using maximum variation purposive sampling to capture a wide range of perspectives and contexts. Results: Thematic analysis revealed that many participants believed societal misperception and prejudices fuel the stigmatisation of TB. Furthermore, misconceptions about the disease’s incurability were widespread, with non-infected participants expressing reluctance to interact with TB patients. In addition, findings indicated that patients often faced isolation from family and society due to stigma. Conclusion: These insights underscore the need for culturally sensitive interventions to bridge healthcare gaps and enhance community awareness about TB and its impact in Bangladesh.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.175
GPT teacher head0.476
Teacher spread0.301 · 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 teacher head, not a consensus.

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

Citations5
Published2024
Admission routes1
Has abstractyes

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