Barriers to and enablers of availability and integration of palliative care into routine services at Charlotte Maxeke Johannesburg Academic Hospital, South Africa
Bibliographic record
Abstract
BACKGROUND: The prevalence of chronic diseases among the South African population is increasing. The incidence of cancers is high and expected to double by 2030 while the HIV burden is disproportionately high. These are indicators of the need for palliative care services in the South African health system. While some palliative care service exists, there are challenges with availability and integration into routine services nationally. In Gauteng Province, CMJAH is one of two quaternary hospitals that provide palliative care services. We sought to find the barriers and enablers for the availability and integration of palliative care into routine services at CMJAH. METHODS: We used an exploratory qualitative research approach. Study sites were the internal medicine, surgical, and medical oncology departments, the palliative care unit, and the administration block at CMJAH. We interviewed four groups of personnel: the management of the four departments, previous and current staff in the palliative care unit, and hospital management staff. A hybrid of inductive and deductive coding was conducted, and the final codebook was developed using all the domains and selected constructs of the Consolidated Framework for Implementation Research (CFIR). RESULTS: Barriers and enablers were categorised according to the domains of the CFIR. We found that individual characteristics such as knowledge and awareness of palliative care, adequate training, and positive attitude towards palliative care encourage the availability of services at a hospital. The availability of a budget for palliative care, a conducive environment for service provision, and management buy-in are also key determinants for palliative care. Poor planning, lack of implementation of the national policy framework and strategy for palliative care, and over-reliance on external donor funds are some of the hinderances for availability and integration of palliative care at a public hospital. CONCLUSION: Despite the importance of palliative care, implementation is a challenge at CMJAH. The availability of a palliative care budget from relevant Government departments, and inclusion of some roles (navigators, spiritual chaplains) in the personnel structure for the Health Department are some of the ways through which availability and integration of the service can be improved across public hospitals in South Africa.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".