Challenges and opportunities in ovarian cancer care: A qualitative study of clinician perspectives from 24 low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer poses a significant and growing burden, particularly in low- and middle-income countries (LMICs) where incidence and mortality are projected to increase by over 50 % by 2050. However, there is a critical lack of qualitative data on the challenges and opportunities for improvement in treatment and care for women with ovarian cancer in these regions. The aim of this research is to investigate clinicians' perspectives on the matter in 24 LMICs. METHODS: As part of the multi-country observational Every Woman Study™ (EWS), semi-structured interviews were conducted with clinicians between June 2022 and June 2023. The interview guide was developed by the EWS LMIC Oversight Committee, including patients, clinicians and data specialists. Relational content and inductive thematic analyses were employed and categories synthesized using the World Health Organization's six building blocks of the Health Systems Framework. RESULTS: 24 clinicians (54 % female; 79 % gynaecologic oncologists, 8 % gynaecologists, 8 % clinical oncologists not specializing in gynaecological cancers, and 4 % clinical oncologists specializing in gynaecological cancers; 42 % from Africa, 29 % from Asia, 29 % from Latin America) participated. Six dominant themes were identified: "Poor Ovarian Cancer Data'', "Inequity in Access to Treatment", "In-Country Inequities in Access to Care", "Role of Cultural Norms on Women's Health", "Increased Engagement of Men in Ovarian Cancer Control", and "Advocacy and Education for Empowering Women". Content analysis revealed system-level challenges such as delayed drug payments, lack of population-based cancer data, and limited imaging facilities. Patient-level challenges included disparities in access to specialists, limited medication affordability, poor symptom recognition, and reliance on alternative treatments. CONCLUSIONS AND POLICY SUMMARY: This study reveals the complexity of ovarian cancer treatment and care in LMICs and the need to mitigate disparities in these regions, underscoring the need for patient-centred, context specific and intersectoral strategies to be considered in cancer planning to improve ovarian cancer care quality and equity in LMICs.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".