Point-of-Care Urinalysis Dipstick Screening for Urinary Tract Infections in Sierra Leone
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".