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Record W4414478921 · doi:10.3390/healthcare13192398

Quality of Care for Hypertension in Primary Health Care in South Africa: Cross-Sectional Feasibility Study

2025· article· en· W4414478921 on OpenAlexaff
Enos M Rampamba, Stephen Campbell, Brian Godman, Johanna C. Meyer

Bibliographic record

VenueHealthcare · 2025
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsAuditPrimary health careHealth carePrimary careBlood pressureMedical recordDescriptive statisticsQuality (philosophy)

Abstract

fetched live from OpenAlex

Introduction: Little is known about the quality of care for patients with hypertension in primary health care (PHC) facilities in South Africa, where most people receive care. Objectives: To test 46 quality indicators, developed previously, to assess and improve care; to assess the indicators’ clinimetric properties; and to recommend improvement strategies. Methods: A descriptive cross-sectional clinical audit in a purposive sample of 12 South African PHC clinics involving a retrospective review of 295 patient medical records. Results: A total of 45 of the 46 indicators were tested in the main sample (n = 295), of which 9 indicators could not be applied. Of the 36 applicable indicators, 22 could be applied and measured for ≥75% of the sample, while 14 were applicable to ≤50% of the sample. Only five indicators showed a quality of care score for ≥75% of patients. Overall, 82% and 92% of the sample had their blood pressure (BP) recorded in the last 12 months or in the previous 5 years for those aged >40, respectively, and 53.2% had a controlled BP. In the last 12 months, 30% of patients had a cholesterol record, 30% had their BMI recorded, 17% had a hypertension review with a medical practitioner, and 12% had received lifestyle advice. Only 38% received all clinically indicated antihypertensive medicines at their last visit. Conclusion: There were gaps in the quality of care for patients with hypertension, demonstrating the need for greater adherence to evidence-based guidelines, better data quality, and the use of electronic health information systems. Twenty-two indicators are recommended to address these gaps and improve the quality of care, patient outcomes, and the health care system.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.135
GPT teacher head0.421
Teacher spread0.286 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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