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Record W4383496112 · doi:10.4103/ijph.ijph_1215_22

Loss of employment and social stigma emerge proxy determinants of patient satisfaction in tuberculosis management under the National Tuberculosis Elimination Program: Reports from a single-center direct observation therapy strategy center in Bhubaneswar, Odisha

2023· letter· en· W4383496112 on OpenAlexaff
Sonali Kar, Melat Menberu, Manas Ranjan Behera, KirtiSundar Sahu

Bibliographic record

VenueIndian Journal of Public Health · 2023
Typeletter
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTuberculosisStigma (botany)Proxy (statistics)MedicineSocial stigmaCenter (category theory)Family medicineSingle CenterPsychiatrySurgeryHuman immunodeficiency virus (HIV)Pathology

Abstract

fetched live from OpenAlex

Dear Editor, India has the world’s highest rate of tuberculosis (TB) infection. To mitigate this burden, the Indian government has been providing free treatment for TB under the direct observation therapy strategy (DOTS) since 1992 under a completely centrally owned program called the Revised National TB Program.[1] Bracing to face the challenge of TB elimination by 2025, the program is rechristened as the National TB Elimination Program (NTEP). The success of the program critically depends on the quality of care in the health-care setting and to suit the patient’s perspective and needs. TB default loss-to-follow-up rate in India ranges from 3% to 17%, which should be < 10%.[2,3] Patients who are satisfied with their clinical consultations or continuum of services are more likely to return to clinics for follow-up therapy and to adhere to treatment requirements. Thus, a measure of patient satisfaction provides input on programmatic performance.[4] The cross-sectional study was conducted at one DOTs center of a tertiary care hospital in Bhubaneswar with the primary objective to measure patient satisfaction with the services rendered under NTEP. This was assessed using a composite score build on 24 questions grading the current NTEP infrastructure, process, and outcome factors using a 5-point Likert scale (very satisfied to very dissatisfied), which was seen to have a cumulative Cronbach’s coefficient of 0.928. A preliminary analysis of the study indicates that employment is significantly associated with satisfaction. Out of 150 respondents, 125 (83%) reported the effect of DOTS service on employment/routine work/studies. A high satisfaction level was obtained on most factors with a weighted mean above 3.4 and dissatisfaction among 21 (14%) of the patients was reported with the transportation cost they paid, while 8 (5%) were not satisfied with a waiting time for treatment score being <3.4. Adherence to daily treatment was compromised among 25 (16%) participants, with diabetes comorbidity being significantly associated with treatment nonadherence (χ2 = 6.541, df = 1, P = 0.01). Patients who faced stigma from friends are 10.58 times less satisfied with DOTS service. TB program in India offers a megamix of diagnostics to drugs to financial compensation for nutrition and incentives for adherence to treatment. However, the disease burden often outnumbers and comprises the outcomes. Robust program delivery is undone by the continued social stigma and perhaps loss of wages incurred to adhere to the daily regime. Often, when the community knows that a person has TB, they debar them from work, which continues in areas of high incidence like Odisha, not understanding that an active case can be a threat to society and spread the infection further.[5] The study strongly hints that patient satisfaction scoring can help identify the weak links in the program and customize it as per local needs. Some social awareness campaigns may be designed, and means of rehabilitation or socioeconomic support should be devised under Rogi Kalyan Samitis or Community Health Associations to support estranged TB patients, especially in rural areas or small cities. TB does not have a social pathology alone but also clearly has social determinants of stigma and economic losses and hence the policymakers need to incorporate these concerns into the programmatic handholding. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

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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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.165
GPT teacher head0.407
Teacher spread0.242 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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