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Record W4413067724 · doi:10.5539/gjhs.v17n4p59

Point-of-Care Urinalysis Dipstick Screening for Urinary Tract Infections in Sierra Leone

2025· article· W4413067724 on OpenAlexfundvenueno aff
Gabrielle Gundermann, S. S. Herrick, Khanjan Mehta

Bibliographic record

VenueGlobal Journal of Health Science · 2025
Typearticle
Language
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsnot available
FundersGrand Challenges Canada
KeywordsDipstickUrinalysisMedicineSierra leoneUrinary systemPoint-of-care testingUrineDescriptive statisticsIntensive care medicineFamily medicineEmergency medicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

Urinary tract infections (UTIs) are prevalent infections especially among women and can result in complications including pyelonephritis, sepsis, and miscarriage. Urine dipstick tests are an alternative diagnostic method to urinalysis for low- and middle-income countries that lack resources for diagnostic testing. This study evaluated the implementation of a UTI screening program with an affordable 3-parameter urine dipstick test in Sierra Leone. The study aimed to determine a healthcare network’s ability to adopt screening, patient acceptability of UTI screening, and patient willingness to pay for screening. Quantitative screening data and qualitative interview data were collected and analysed via a multiple linear regression and general inductive approach, respectively. The results suggest higher earning occupations and engagement of community leadership in sensitization are associated with increased willingness of patients to undergo screening. Furthermore, screening willingness only decreased at the highest cost level, and a consensus existed on an optimal price point. Qualitative results supported implementation through general clinic operations with a systematic screening process.

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)0.006
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0010.001
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 head0.032
GPT teacher head0.384
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

Citations0
Published2025
Admission routes2
Has abstractyes

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