The burden of traveling for cervical cancer treatment in Uganda: a mixed-method study
Bibliographic record
Abstract
Abstract BACKGROUND Uganda has one of the highest rates of cervical cancer in the world. Many women are diagnosed and treated with advanced stages of the disease. With only one facility offering comprehensive cervical cancer care in Uganda, many women are required to travel significant distances and spend time away from their homes to receive cervical cancer care. It is important to understand the burden of time away from home while attending treatment because it can inform the expansion of cervical cancer screening programs. The aim of this mixed-methods paper is to describe how the distance to cervical cancer treatment locations impact women in Uganda. METHODS Women were recruited from September 19, 2022, to January 17, 2023 at the Uganda Cancer Institute (UCI) and the cancer clinic at Jinja Regional Referral Hospital (JRRF). Women were eligible for the study if they were (i) aged ≥18 years with a histopathologic diagnosis of cervical cancer; (ii) being treated at the UCI or JRRF for cervical cancer; and (iii) able to provide consent to participate in the study in English, Luganda, Lusoga, Luo, or Runyankole. All participants completed a quantitative survey and a selected group were sampled for semi-structured interviews. Data was analyzed using the convergent parallel mixed-methods approach. Descriptive statistics were reported for the quantitative data and qualitative data using an inductive-deductive thematic analysis approach. RESULTS In all, 351 women participated in the quantitative section of the study and 24 in the qualitative. The quantitative and qualitative findings largely aligned and support one another. Women reported traveling up to 14 hours to receive treatment and 20% noted that they would spend three or more nights away from home during their current visit. Major themes of the qualitative include means of transportation, spending the night away from home, and financial factors. CONCLUSION Our findings show that traveling to obtain cervical cancer care can be a significant burden for women in Uganda. Approaches should be considered to reduce this burden such as additional satellite cervical cancer clinics or subsidized transportation options.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".