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Record W2915042228 · doi:10.1177/2325958218823285

Health System Factors Constrain HIV Care Providers in Delivering High-Quality Care: Perceptions from a Qualitative Study of Providers in Western Kenya

2019· article· en· W2915042228 on OpenAlexaff
Becky L. Genberg, Juddy Wachira, Catherine Kafu, Ira B. Wilson, Beatrice Koech, Regina Kamene, Jacqueline Akinyi, Jennifer Knight, Paula Braitstein, Norma C. Ware

Bibliographic record

VenueJournal of the International Association of Providers of AIDS Care (JIAPAC) · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthU.S. President’s Emergency Plan for AIDS Relief
KeywordsMedicineThematic analysisQualitative researchHealth careNursingHuman immunodeficiency virus (HIV)Family medicine

Abstract

fetched live from OpenAlex

The burden on health systems due to increased volume of patients with HIV continues to rapidly increase. The goal of this study was to examine the experiences of HIV care providers in a high patient volume HIV treatment and care program in eastern Africa. Sixty care providers within the Academic Model Providing Access to Healthcare program in western Kenya were recruited into this qualitative study. We conducted in-depth interviews focused on providers' perspectives on health system factors that impact patient engagement in HIV care. Results from thematic analysis demonstrated that providers perceive a work environment that constrained their ability to deliver high-quality HIV care and encouraged negative patient-provider relationships. Providers described their roles as high strain, low control, and low support. Health system strengthening must include efforts to improve the working environment and easing burden of care providers tasked with delivering antiretroviral therapy to increasing numbers of patients in resource-constrained settings.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.022
GPT teacher head0.361
Teacher spread0.339 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations31
Published2019
Admission routes1
Has abstractyes

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