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Record W2981753769 · doi:10.1016/j.jctube.2019.100127

User-experience and patient satisfaction with quality of tuberculosis care in India: A mixed-methods literature review

2019· review· en· W2981753769 on OpenAlexaff
Himani Bhatnagar

Bibliographic record

VenueJournal of Clinical Tuberculosis and Other Mycobacterial Diseases · 2019
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicinePatient satisfactionTuberculosisQuality (philosophy)Patient experienceMedical physicsSurgeryHealth carePathology

Abstract

fetched live from OpenAlex

Tuberculosis affected 2.7 million people in India in 2017. The Revised National TB Control Programme has achieved milestones in coverage, however quality of TB care remains highly variable and often poor, with significant gaps in provider knowledge, practices, and patients consistently lost to follow-up. These quality gaps are largely informed by studies on provider practices or objective chart abstractions and case data. Per the knowledge of the author, no review has been conducted on first-hand patient perspectives on the quality of TB care they receive. This mixed-methods literature review aims to synthesize evidence on user-experience and patient satisfaction with TB care in India and inform areas for service quality improvement. Five medical databases, including PubMed, EMBASE, Global Health (Ovid), Web of Science, and CINAHL were searched for empirical studies on patient perspectives on TB health services published between January 1st, 2000 to December 31st, 2017. Studies in English with adult patients with any form of TB in the public or private health system were included. Studies prior to entering the health system, on distance to health facilities and cost were excluded. Seven Indian journals were hand searched and a grey literature search was conducted in GoogleScholar. Studies were assessed for methodological quality and thematic analysis was conducted by categorizing data using NVivo 12. A total of 498 studies were screened, of which 23 met the inclusion criteria. 16 supplementary studies were identified from Indian journals and grey literature. Of the 39 total studies included most were quantitative (29; 74%), based in South India (17; 44%) and focused on drug-sensitive TB patients (19; 49%) within the public health system (25; 64%). Data collection methods were highly heterogenous which limited synthesis and comparisons across population demographics, health sectors, or regions. Overall quantitative patient satisfaction measured in seven studies was high. Two major themes identified were provider-related factors (n = 26 studies) and convenience (n = 25), and six minor themes were supplies and equipment availability (n = 12), confidence (n = 10), information and communication (n = 10), waiting time (n = 8), stigma (n = 4), and confidentiality (n = 4). Each reported positive and negative user-experiences. Most significantly, DOTS did not fit the daily needs and obligations of many patients, particularly due to conflicts with employment and frequency of visits; while positive provider support, information, and flexibility helped patients adhere to treatment. Although quantitative patient satisfaction was found to be high, data were not collected using robust, validated tools. Qualitative and quantitative user-experiences in each theme were variable, making them both barriers and facilitators of good quality TB care. Poor user-experiences were often responsible for patients interrupting treatment or dropping out of TB care. Patient-centeredness, or user-friendliness of TB care can be improved by introducing individualized or flexible DOTS that is responsive to user circumstances and needs. User-experience data should be systematically collected using a standardized, national tool for identification of specific bottlenecks and successes in quality of TB care from the patients’ perspective.

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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.729
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.069
GPT teacher head0.487
Teacher spread0.418 · 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
GenreReview

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

Citations24
Published2019
Admission routes1
Has abstractyes

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