Health care waste management in community-based care: experiences of community health workers in low resource communities in South Africa
Bibliographic record
Abstract
BACKGROUND: In South Africa, community health workers (CHWs) working in community-based care (CBC) programmes provide care to patients most of whom are living with HIV/AIDS and tuberculosis (TB). Although studies have shown that the caregiving activities provided by the CHWs generate health care waste (HCW), there is limited information about the experiences of CHWs on health care waste management (HCWM) in CBC. This study explored HCWM in CBC in Durban, South Africa from the perspectives CHWs. METHODS: We used three ethnographic approaches to collect data: focus group discussions, participant observations and informal discussions. Data was collected from 85 CHWs working in 29 communities in the Durban metropolis, South Africa. Data collection took place from July 2013 to August 2014. RESULTS: CHWs provided nursing care activities to patients many of whom were incontinent or bedridden. Some the patients were living with HIV/AIDS/TB, stroke, diabetes, asthma, arthritis and high blood pressure. These caregiving activities generate sharps and infectious waste but CHWs and family members did not segregate HCW according to the risk posed as stipulated by the HCWM policy. In addition, HCW was left with domestic waste. Major barriers to proper HCWM identified by CHWs include, lack of assistance from family members in assisting patients to use the toilet or change diapers and removing HCW from homes, irregular waste collection by waste collectors, inadequate water for practicing hygiene and sanitation, long distance between the house and the toilets and poor conditions of communal toilets and pit latrines. As a result of these barriers, HCW was illegally dumped along roads or in the bush, burnt openly and buried within the yards. Liquid HCW such as vomit, urine and sputum were disposed in open spaces near the homes. CONCLUSION: Current policies on primary health care (PHC) and HCWM in South Africa have not paid attention to HCWM. Findings suggest the need for primary health care reform to develop the competencies of CHWs in HCWM. In addition, PHC and HCWM policies should address the infrastructure deficit in low resource communities. In order for low-and-middle-income-countries (LMICs) to develop effective community health worker programmes, there is a need for synergies in PHC and HCWM policies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".