Protecting health workers from infectious disease transmission: an exploration of a Canadian-South African partnership of partnerships
Bibliographic record
Abstract
BACKGROUND: Health workers are at high risk of acquiring infectious diseases at work, especially in low and middle-income countries (LMIC) with critical health human resource deficiencies and limited implementation of occupational health and infection control measures. Amidst increasing interest in international partnerships to address such issues, how best to develop such collaborations is being actively debated. In 2006, a partnership developed between occupational health and infection control experts in Canada and institutions in South Africa (including an institute with a national mandate to conduct research and provide guidance to protect health workers from infectious diseases and promote improved working conditions). This article describes the collaboration, analyzes the determinants of success and shares lessons learned. METHODS: Synthesizing participant-observer experience from over 9 years of collaboration and 10 studies already published from this work, we applied a realist review analysis to describe the various achievements at global, national, provincial and hospital levels. Expectations of the various parties on developing new insights, providing training, and addressing service needs were examined through a micro-meso-macro lens, focusing on how each main partner organization contributed to and benefitted from working together. RESULTS: A state-of-the-art occupational health and safety surveillance program was established in South Africa following successful technology transfer from a similar undertaking in Canada and training was conducted that synergistically benefitted Northern as well as Southern trainees. Integrated policies combining infection control and occupational health to prevent and control infectious disease transmission among health workers were also launched. Having a national (South-South) network reinforced by the international (North-south) partnership was pivotal in mitigating the challenges that emerged. CONCLUSIONS: High-income country partnerships with experience in health system strengthening - particularly in much needed areas such as occupational health and infection control - can effectively work through strong collaborators in the Global South to build capacity. Partnerships are particularly well positioned to sustainably reinforce efforts at national and sub-national LMIC levels when they adopt a "communities of practice" model, characterized by multi-directional learning. The principles of effective collaboration learned in this "partnership of partnerships" to improve working conditions for health workers can be applied to other areas where health system strengthening is needed.
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 | 0.031 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.040 | 0.014 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".