Occupational Health Barriers in South Africa: A Call for Ubuntu
Bibliographic record
Abstract
Many low- and middle-income countries (LMICs) grapple with shortages of health workers, a crucial component of robust health systems. The COVID-19 pandemic underscored the imperative for appropriate staffing of health systems and the occupational health (OH) threats to health workers. Issues related to accessibility, coverage, and utilization of OH services in public sector health facilities within LMICs were particularly accentuated during the pandemic. This paper draws on the observations and experiences of researchers engaged in an international collaboration to consider how the South African concept of Ubuntu provides a promising way to understand and address the challenges encountered in establishing and sustaining OH services in public sector health facilities. Throughout the COVID-19 pandemic, the collaborators actively participated in implementing and studying OH and infection prevention and control measures for health workers in South Africa and internationally as part of the World Health Organizations' Collaborating Centres for Occupational Health. The study identified obstacles in establishing, providing, maintaining and sustaining such measures during the pandemic. These challenges were attributed to lack of leadership/stewardship, inadequate use of intelligence systems for decision-making, ineffective health and safety committees, inactive trade unions, and the strain on occupational health professionals who were incapacitated and overworked. These shortcomings are, in part, linked to the absence of the Ubuntu philosophy in implementation and sustenance of OH services in LMICs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".