Preparing the healthcare workforce in South Africa for short-course rifampicin-resistant TB treatment: Inter-professional training and task-sharing considerations
Bibliographic record
Abstract
Abstract Background: Treatment for rifampicin-resistant Mycobacterium tuberculosis (RR-TB) is complex, however, shorter treatment, with newer antimicrobials are improving treatment outcomes. The South African National Department of Health (NDoH) recently accelerated the rollout of 9-month, all oral, RR-TB short-course regimens. We sought to evaluate an inter-professional training program using pre-test and post-test performance of Professional Nurses (PNs), Advanced Practice Professional Nurses (APPNs) and Medical Officers (MOs) to inform: a) training needs across cadres; b) knowledge performance, by cadres; and c) training differences in knowledge by nurse type. Methods: A 4-day didactic and case-based clinical decision support course for RR-TB regimens in South Africa (SA) was developed, reviewed and nationally accredited. Between February 2017 and July 2018, 12 training events were held. Clinicians who may initiate RR-TB treatment, specifically MOs and PN/APPNs with matched pre-post tests and demographic surveys were analyzed. Descriptive statistics are provided. Pre-post test evaluations included 25 evidence-based clinically related questions about RR-TB diagnosis, treatment, and care. Results: Participants (N=842) participated in testing, and matched evaluations were received for 800 (95.0%) training participants. Demographic data was available for 793 (99.13%) participants, of whom 762 (96.1%) were MOs, or nurses, either PN or APPNs. Average correct response pre-test and post-test scores were 61.7% (range 7-24 correct responses) and 85.9% (range 12-25), respectively. Overall, 95.8% (730/762) of participants demonstrated improved knowledge. PNs improved on average 25% (6.22 points) whereas MOs improved 10% (2.89 points) with better mean test scores on both pre- and post-test (p<0.000). APPNs performed the same as the MOs on post-test scores (p=NS). Conclusions: The inter-professional training program in short-course RR-TB treatment improved knowledge for participants. MOs had significantly greater pre-test scores. Of the nurses, APPNs outperformed other PNs, and performed equally to MOs on post-test scores, suggesting this advanced cadre of nurses might be the most appropriate to initiate and monitor treatment in close collaboration with MOs. All cadres of nurse reported the need for additional clinical training and mentoring prior to managing such patients.
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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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.009 |
| 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".