Perspectives of policy-makers and stakeholders about health care waste management in community-based care in South Africa: a qualitative study
Bibliographic record
Abstract
BACKGROUND: In South Africa, a new primary health care (PHC) re-engineering initiative aims to scale up the provision of community-based care (CBC). A central element in this initiative is the use of outreach teams comprising nurses and community health workers to provide care to the largely poor and marginalised communities across the country. The provision of care will inevitably lead to an increase in the amount of health care waste (HCW) generated in homes and suggests the need to pay more attention to the HCW that emanates from homes where there is care of a patient. CBC in South Africa is guided by the home-based care policy. However, this policy does not deal with issues about how HCW should be managed in CBC. This study sought to explore health care waste management (HCWM) in CBC in South Africa from the policy-makers' and stakeholders' perspective. METHODS: Semi-structured interviews were conducted with 9 policy-makers and 21 stakeholders working in 29 communities in Durban, South Africa. Interviews were conducted in English; were guided by an interview guide with open-ended questions. Data was analysed thematically. RESULTS: The Durban Solid waste (DSW) unit of the eThekwini municipality is responsible for overseeing all waste management programmes in communities. Lack of segregation of waste and illegal dumping of waste were the main barriers to proper management practices of HCW at household level while at the municipal level, corrupt tender processes and inadequate funding for waste management programmes were identified as the main barriers. In order to address these issues, all the policy-makers and stakeholders have taken steps to collaborate and develop education awareness programmes. They also liaise with various government offices to provide resources aimed at waste management programmes. CONCLUSIONS: HCW is generated in CBC and it is poorly managed and treated as domestic waste. With the rollout of the new primary health care model, there is a greater need to consider HCWM in CBC. There is need for the Department of Health to work together with the municipality to ensure that they devise measures that will help to deal with improper HCWM in the communities.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".