Abstract 58: Acceptability of an mHealth-Based Strategy to Improve Attendance at Cervical Cancer Screening in Low- Resource Settings
Bibliographic record
Abstract
Abstract Purpose: There is a need for innovative evidence-based strategies facilitating access to cervical cancer screening (CCS) in low-resource settings. Research on the use of mobile health (mHealth) for recruitment to improve CCS accessibility is limited. The study aim was to explore the acceptability and willingness to receive text messages about future CCS availability in a low-resource setting. Methods: ASPIRE Mayuge (2019–2021) was a two armed cluster randomized trial conducted in 31 villages in the rural Mayuge district of Uganda, that compared the effectiveness of two recruitment implementation strategies offering self-collection for CCS (door-to-door vs community health days). Women aged 25–49 with no history of hysterectomy or treatment for cervical cancer (CC) or precancer were recruited. Both arms were implemented via Village Health Teams (VHTs). VHTs administered a baseline survey that included questions on mHealth. Differences were determined using Fisher’s exact tests and p values ≤0.05 were considered statistically significant. Results: Of the 2,050 women included in this analysis, 95.2% reported interest in receiving a text message with information on when CCS would be available at the health centre. 91.3% of women reported having access to a phone, with 82.1% of them owning their own phone. Women with access to a phone were more likely to report interest in receiving a text (94.4% willing vs 60.9% unwilling; p<0.0001). Those who owned their own phone were also more likely to be willing to receive a text (83.1% willing vs 51.8% unwilling, p<0.0001). There was high prior CC knowledge (91.7%) and health centre visit history (81.2%) among those who reported interest in receiving a text. Conclusion: These findings show that women were highly willing to receive a text about future CCS availability. Those with phone access and phone ownership were more likely to respond favorably to receiving texts. High phone access and acceptability demonstrated in this rural setting indicates potential for implementation and scalability of mHealth strategies to improve CCS attendance in low-resource contexts. Further research into the viability of these methods in various settings is required before integration into CCS programs. Citation Format: Ashwini Prabhakaran, Smritee Dabee, Candice Ruck, Nadia Mithani, Anna Gottschlich, Priscilla Naguti, Laurie W. Smith, Carolyn Nakisige, Gina S. Ogilvie. Acceptability of an mHealth-Based Strategy to Improve Attendance at Cervical Cancer Screening in Low- Resource Settings [abstract]. In: Proceedings of the 13th Annual Symposium on Global Cancer Research; 2025 Sep 16. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(12_Suppl):Abstract nr 58.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".