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Record W3006165305 · doi:10.1016/j.jctube.2020.100154

User experience and patient satisfaction with tuberculosis care in low- and middle-income countries: A systematic review

2020· review· en· W3006165305 on OpenAlexaff
Danielle Cazabon, Tripti Pande, Paulami Sen, Amrita Daftary, Catherine Arsenault, Himani Bhatnagar, Kate O’Brien, Madhukar Pai

Bibliographic record

VenueJournal of Clinical Tuberculosis and Other Mycobacterial Diseases · 2020
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcMaster UniversityYork UniversityMcGill University
FundersBill and Melinda Gates Foundation
KeywordsMedicineTuberculosisLow and middle income countriesPatient satisfactionDeveloping countrySurgeryPathologyEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: Patient-centered care is at the forefront of the End TB strategy, yet little is known about user (patient's) experience and patient satisfaction with TB services. Our study aims to systematically review quantitative studies evaluating user experience and TB patient satisfaction within the health care system. METHODS: Five medical databases were systematically searched between January 1st, 2009 and December 31st, 2018. English studies assessing user experience and patient satisfaction within the healthcare system from a TB patient's perspective in low and middle-income countries, were included. RESULTS: = 19). Overall study quality was low as they used varying tools to measure user experience and patient satisfaction. CONCLUSION: Our study shows large variability in measurement of user experiences and patient satisfaction. Studies reported that patients were mostly satisfied with TB care services, and those that were dissatisfied were substantially more likely to be lost to follow-up. The high satisfaction rates could have been due to lack of education on good quality patient care or fear of losing access to health care. A standardized patient centered tool could be designed to help assess user experience and patient satisfaction to allow comparisons among health systems and countries.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.389
Teacher spread0.346 · 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 designSystematic review
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

Citations33
Published2020
Admission routes1
Has abstractyes

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