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Record W2955309789 · doi:10.1016/j.jctube.2019.100111

In the eye of the multiple beholders: Qualitative research perspectives on studying and encouraging quality of TB care in India

2019· article· en· W2955309789 on OpenAlexafffund
Andrew McDowell, Nora Engel, Amrita Daftary

Bibliographic record

VenueJournal of Clinical Tuberculosis and Other Mycobacterial Diseases · 2019
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
FundersEuropean Research CouncilNederlandse Organisatie voor Wetenschappelijk OnderzoekFonds de Recherche du Québec - SantéEngineering Research Centers
KeywordsMedicineQuality (philosophy)Eye careQualitative researchOptometryEpistemology

Abstract

fetched live from OpenAlex

This paper outlines insights qualitative research brings to the study of quality of care. It advocates understanding care as sequential, interpersonal action aimed at improving health and documenting the networks in which care occurs. It assesses the strengths and weakness of contemporary quantitative and qualitative approaches to examining quality of care for tuberculosis (TB) before outlining three qualitative research programs aimed at understanding quality of TB in India. Three case studies focus on the diagnosis level in the cascade of TB care and use qualitative research to examine the clinical use of pharmaceuticals as diagnostics, the development of diagnostic tests, and the role of care providers in the utilization of diagnostic services. They show that 1) care must be understood as part of relationships over time, 2) the presence or absence of technologies does not always imply their expected use in care, 3) physicians' provision of care is often inflected by their perceptions of patient desires, and 4) effective care is not always perfectly aligned with global health priorities. Qualitative methods with a networked perspective on care provide novel findings that can and have been used when developing quality of care improvement interventions for TB.

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)0.008
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.182
GPT teacher head0.534
Teacher spread0.352 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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