Leadership, cohesion, and stress in primary care facilities and retention in chronic care in rural northeast South Africa before and during the COVID-19 pandemic: A longitudinal study
Bibliographic record
Abstract
Background: Human immunodeficiency virus (HIV) and hypertension are major contributors to morbidity and mortality in South Africa. Effective management of these conditions is critical to population health, yet patient management and retention varies by facility for reasons that are not fully understood. We assessed whether measures of clinic leadership, cohesion, and stress were associated with retention for HIV and hypertension in a cohort of patients in northeast South Africa before and during the Coronavirus disease 2019 pandemic. Methods: We quantified nursing capacity and service readiness within primary health care facilities in the Bushbuckridge sub-district in Mpumalanga province South Africa. We administered brief scales on facility leadership, cohesion, and stress from January to March 2019, and tested scales for individual and facility-level agreement. We extracted clinical records for patients with HIV and/or hypertension from 2019 to 2021 and quantified treatment retention by quarter. We used generalised estimating equations to assess individual and clinic factors associated with retention in each treatment programme prior to (2019-first quarter 2020) and during (second quarter 2020-2021) the pandemic. Results: The nine facilities had a median of 12 nurses on staff and scored 0.83 out of 1.0 on basic service readiness. We collected responses to leadership, cohesion, and stress scales from 54 nurses and counsellors. Scales showed high inter-item agreement and moderate within-facility agreement. From 2019 to 2021, 19 445 individuals were treated for HIV and/or hypertension across seven participating facilities. Two-year retention was 91% for those with both conditions, 82% for those in treatment for HIV alone and 77% for those in treatment for hypertension alone, with 10-15% differences between facilities and high retention during the pandemic period. In addition to those with both conditions, women and adults aged 60-69 were more likely to be retained. Clinic factors were inconsistently associated with patient retention. Conclusions: While measures of clinic leadership, cohesion, and stress were generally reliable at individual and facility levels, we found limited evidence supporting a link between these factors and better retention in care. Retention was stable during the Coronavirus disease 2019 pandemic. Men, the youngest and oldest adults, and those without known multimorbidity should be prioritised for retention interventions.
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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 | 0.001 |
| 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".