Investigating the Healthcare-Seeking Behaviors of Mobile Phone Users to Improve Cervical Cancer Screening in Rural Uganda
Bibliographic record
Abstract
PURPOSE Cervical cancer is the leading cause of cancer in low- and middle-income countries, despite being a preventable disease. Uganda, which lacks an effective screening program, has one of the highest cervical cancer incidence rates in the world. However, mobile health (mHealth) technology has the potential to improve healthcare-seeking behaviors and access to cervical cancer screening. This study aims to describe the connection between mobile phone access and healthcare-seeking behaviors in rural Uganda. METHODS This cross-sectional study recruited participants from January 23 to August 24, 2023. Women were eligible if they had no prior screening or treatment for cervical cancer in the past 5 years, were aged 30 to 49 years old, and were residents of the South Busoga Forest reserve. Each participant completed a 43-item survey which included questions on demographics, previous health service usage, and opinions on cervical cancer prevention. Bivariate data was analyzed using descriptive statistics and chi-square tests. RESULTS Of the 1434 participants included in the analysis, 91.4% (n = 1310) reported having access to a mobile phone. The majority of participants were aged 30-40 years (n = 929, 64.8%), reported being married or in a relationship (access, n = 1133, 86.5% no access, n = 106, 85.5% p= 0.948), had ≤ primary education (access, n = 1157, 88.3% no access, n = 114, 91.9% p=0.434), and reported farming as their occupation (access, n = 1114, 85.0% no access, n = 101, 81.5% p=0.438). 1143 (87.3%) of individuals in the access group and 90 (72.6%) of those in the no access group reported ever attending a healthcare outreach visit (p<0.001). Additionally, 96.9% (n = 1269) of the access group and 93.5% (n = 116) of the no access group reported that they had ever visited a health center (p<0.001). CONCLUSION While attendance to healthcare outreach visits or health centers was high amongst participants, those with mobile phone access were more likely to seek healthcare services. Further inquiry into this association between mobile phone access and healthcare-seeking behavior is needed to optimize the improvements to cervical cancer screening when implementing interventions such as mHealth technology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".