Model for Achieving a Coordinated Access to Lung Cancer Care in Selected Public Health Facilities in KwaZulu-Natal, South Africa: Protocol for a Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Timely delivery of high-quality cancer care to all patients is barely achieved in South Africa and many other low- and middle-income countries, mainly due to poor care coordination and access to care services. After health care visits, many patients leave facilities confused about their diagnosis, prognosis, options for treatment, and the next steps in their care continuum. They often find the health care system disempowering and inaccessible, thereby making access to health care services inequitable, with the resultant outcome of increased cancer mortality rates. OBJECTIVE: The aim of this study is to propose a model for cancer care coordination interventions that can be used to guide and achieve coordinated access to lung cancer care in the selected public health care facilities in KwaZulu-Natal. METHODS: This study will be conducted through a grounded theory design and an activity-based costing approach that will include health care providers, patients, and their caregivers. The study participants will be purposively selected, and a nonprobability sample will be selected based on characteristics, experiences of the health care providers, and the objectives of the study. With the study's objectives in mind, communities in Durban and Pietermaritzburg were selected as study sites, for the study along with the 3 public health facilities that provide cancer diagnosis, treatment, and care in the province. The study involves a range of data collection techniques, namely, in-depth interviews, evidence synthesis reviews, and focus group discussions. A thematic and cost-benefit analysis will be used. RESULTS: This study receives support from the Multinational Lung Cancer Control Program. The study obtained ethics approval and gatekeeper permission from the University's Ethics Committee and the KwaZulu-Natal Provincial Department of Health, as it is being conducted in health facilities in KwaZulu-Natal province. As of January 2023, we had enrolled 50 participants, both health care providers and patients. Dissemination activities will involve community and stakeholder dissemination meetings, publications in peer-reviewed journals, and presentations at regional and international conferences. CONCLUSIONS: This study will provide comprehensive data to inform and empower patients, professionals, policy architects, and related decision makers to manage and improve cancer care coordination. This unique intervention or model will address the multifactorial problem of cancer health disparities. If successful, this study will affect the design and implementation of coordination programs to promote optimal cancer care for underserved patients. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34341.
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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 | 0.035 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".