The role of triage to reduce waiting times in primary health care facilities in the North West province of South Africa
Bibliographic record
Abstract
BACKGROUND: Worldwide, patients visiting health care facilities in the public health care sector have to wait for attention from health care professionals. In South Africa, the Cape Triage Score system was implemented successfully in hospitals' emergency departments in the Cape Metropole. The effective utilisation of triage could improve the flow of primary health care (PHC) patients and direct the patients to the right health care professional immediately. AIM: No literature could be traced on the implementation of triage in PHC facilities in South Africa. Consequently, a study addressing this issue could address this lack of information, reduce waiting times in PHC facilities and improve the quality of care. SETTING: PHC facilities in a sub-district of the North West province of South Africa. METHOD: A quantitative, exploratory, typical descriptive pre-test-post-test design was used. The study consisted of two phases. During phase 1, the waiting time survey checklist was used to determine the baseline waiting times. In phase 2, the Cape Triage Score system that triaged the patients and the waiting time survey checklist were used. RESULTS: Data were analysed using Cohen's effect sizes by comparing the total waiting times obtained in both phases with the waiting time survey checklist. Results indicated no reduction in the overall waiting time; however, there was a practical significance where triage was applied. Referral was much quicker to the correct health professional and to the hospitals. CONCLUSION: Although the results indicated no reduction in the overall waiting time of patients, structured support systems and triage at PHC facilities should be used to make referral quicker to the correct health professional and to the hospitals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".