Implementing nurse-initiated and managed antiretroviral treatment (NIMART) in South Africa: a qualitative process evaluation of the STRETCH trial
Bibliographic record
Abstract
BACKGROUND: Task-shifting is promoted widely as a mechanism for expanding antiretroviral treatment (ART) access. However, the evidence for nurse-initiated and managed ART (NIMART) in Africa is limited, and little is known about the key barriers and enablers to implementing NIMART programmes on a large scale. The STRETCH (Streamlining Tasks and Roles to Expand Treatment and Care for HIV) programme was a complex educational and organisational intervention implemented in the Free State Province of South Africa to enable nurses providing primary HIV/AIDS care to expand their roles and include aspects of care and treatment usually provided by physicians. STRETCH used a phased implementation approach and ART treatment guidelines tailored specifically to nurses. The effects of STRETCH on pre-ART mortality, ART provision, and the quality of HIV/ART care were evaluated through a randomised controlled trial. This study was conducted alongside the trial to develop a contextualised understanding of factors affecting the implementation of the programme. METHODS: This study was a qualitative process evaluation using in-depth interviews and focus group discussions with patients, health workers, health managers, and other key informants as well as observation in clinics. Research questions focused on perceptions of STRETCH, changes in health provider roles, attitudes and patient relationships, and impact of the implementation context on trial outcomes. Data were analysed collaboratively by the research team using thematic analysis. RESULTS: NIMART appears to be highly acceptable among nurses, patients, and physicians. Managers and nurses expressed confidence in their ability to deliver ART successfully. This confidence developed slowly and unevenly, through a phased and well-supported approach that guided nurses through training, re-prescription, and initiation. The research also shows that NIMART changes the working and referral relationships between health staff, demands significant training and support, and faces workload and capacity constraints, and logistical and infrastructural challenges. CONCLUSIONS: Large-scale NIMART appears to be feasible and acceptable in the primary level public sector health services in South Africa. Successful implementation requires a comprehensive approach with: an incremental and well supported approach to implementation; clinical guidelines tailored to nurses; and significant health services reorganisation to accommodate the knock-on effects of shifts in practice.
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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 | 0.086 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".