Task shifting of triage to peer expert informal care providers at a tertiary referral HIV clinic in Malawi: a cross-sectional operational evaluation
Bibliographic record
Abstract
HIV treatment models in Africa are labour intensive and require a high number of skilled staff. In this context, task-shifting is considered a feasible alternative for ART service delivery. In 2006, a lay health cadre of expert patients (EPs) at a tertiary referral HIV clinic in Zomba, Malawi was capacitated. There are few evaluations of EP program efficacy in this setting. Triage is the process of prioritizing patients in terms of the severity of their condition and ensures that no harmful delays occur to treatment and care. This study evaluates the safety of task-shifting triage, in an ambulatory low resource setting, to EPs. As a quality improvement exercise in April 2010, formal triage training was conducted by adapting the World Health Organization Emergency Triage Assessment and Treatment Triage Module Guidelines. A cross sectional observation study was conducted 2 years after the intervention. Triage assessments performed by EPs were repeated by a clinical officer (gold standard) to assess sensitivities, specificities, positive and negative predictive values for EP triage scores. Proportions were calculated for categories of disposition by stratifying by EP and clinician triage scores. A total of 467 patients were triaged by 7 EPs and re-triaged by clinical officers. With combined triage scores for emergency and priority patients we report a sensitivity of 85% and specificity of 74% for the EP scoring, with a low positive predictive value (41%) and a high negative predictive value (96%). We calculate a serious miss rate of EP scoring (i.e. missed priority or emergency patients) as 2.2%. Admission rates to hospital were highest among those patients triaged as emergency cases either by the EP's (21%) or the clinicians (83%). Fewer patients triaged as priority by either EPs (5%) or clinicians (15%) were admitted to hospital, however these patients had the highest prevalence of same day lab testing and/or specialty referral. Our study provides reassurance that in the context of adequate training and ongoing supervision, task-shifting triage to lay health care workers does not necessarily lead to less accurate triaging. EPs have a tendency to be more conservative in over-triaging patients.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".