A Resiliency Intervention to Support Nurses Engaged in the Provision of HIV Care in KwaZulu-Natal, South Africa: Protocol for a pilot Randomized Control Trial (Preprint)
Bibliographic record
Abstract
Background: South Africa has the largest HIV epidemic in the world; in KwaZulu-Natal Province, over 40.8% of adults aged 15 years and older are living with HIV. Despite this, South Africa is home to only 3% of the world's health care workers. Nurses constitute the largest group of providers in South Africa and experience high levels of burnout, which can contribute to negative patient outcomes for people living with HIV, including reduced treatment adherence. Nurse-centered interventions that offset these effects are urgently needed. Objective: This study aims to test the feasibility and acceptability of an adapted resiliency intervention (Stress Management and Resiliency Training-Relaxation Response Resiliency Program) for professional nurses who provide care for people living with HIV in South Africa. Methods: In phase 1 (Human Research Ethics Committee [Medical] of the University of the Witwatersrand [220813, Johannesburg, South Africa] and the Massachusetts General Brigham Institutional Review Board [2022P002765, Boston, Massachusetts, United States]), we conducted 3 focus group discussions to solicit feedback on the lived experiences of stress, sources of stress, impact on job functioning, coping strategies, the proposed intervention, and recruitment strategies for nurses. These data informed adaptations to the intervention. In phase 2 (Human Research Ethics Committee [240106, Johannesburg, South Africa]; Massachusetts General Brigham Institutional Review Board [2024P001407, Boston, Massachusetts, United States]), we conducted a small proof-of-concept study (N=8) with preintervention and postintervention assessments, 6 intervention sessions with a nurse interventionist, and a qualitative exit interview. Following appropriate adaptations, we conducted a pilot randomized controlled trial (N=60) in which participants were randomized to the intervention or the control condition. The control condition received a one-time, 90-minute didactic stress management session. The intervention condition consisted of two 4-hour group skills-based sessions on the relaxation response, components of stress, recuperative sleep, mindful awareness, resilience, and social support. Sessions included practice-based exercises and videos to complement the intervention materials. Baseline, posttreatment (intervention only), and follow-up assessments, as well as qualitative exit interviews (n=15, intervention only), were conducted. Primary outcomes are feasibility (number screened, eligible, and enrolled; the number of treatment sessions and assessments completed in the intervention arm; assessment duration; and reasons for declining enrollment and prematurely leaving the trial) and acceptability (Client Satisfaction Questionnaire-8 and qualitative data). Results: The project is funded by the National Institute of Mental Health (R34MH126753; September 2022). As of October 2025, we have completed both the proof-of-concept study (n=8; February 2025) and the pilot randomized controlled trial (n=60; August 2025). Data analysis is in progress and is expected to be completed in August 2026. Conclusions: Structural changes are needed to ensure the well-being of health care providers; however, given that structural changes take time, money, and political capital to execute, we must develop interventions to support providers' mental health while advocating for systematic change.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.006 |
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".