Increasing the Uptake of Breast and Cervical Cancer Screening Via the MAwar Application: Stakeholder-Driven Web Application Development Study
Bibliographic record
Abstract
Background: Digital health interventions such as web health applications significantly enhance screening accessibility and uptake, particularly for individuals with low literacy and income levels. By involving stakeholders-including health care professionals, patients, and technical experts-an intervention can be tailored to effectively meet the users' needs, ensuring contextual relevance for better acceptance and impact. Objective: The aim of this study is to prioritize the content and user interface appropriate for developing a web health application, known as the MAwar app, to promote breast and cervical cancer screening. Methods: A cross-sectional study for stakeholder engagement was conducted to develop a web-based application known as the MAwar app as part of a larger study entitled "The Effectiveness of an Interactive Web Application to Motivate and Raise Awareness on Early Detection of Breast and Cervical Cancers (The MAwar study)". The stakeholder engagement process was conducted in a public health district that oversees 12 public primary care clinics with existing cervical and breast cancer screening programs. We purposively selected the stakeholders for their relevant roles in breast and cervical cancer screening (health care staff, patients, and public representatives), as well as expertise in software and user interface design (technology experts). The Quality Function Deployment method was used to reflect the priorities of diverse stakeholders (health care, technology experts, patients, and public representatives) in its design. The Quality Function Deployment method facilitated the translation of stakeholder perspectives into app features. Stakeholders rated features on a scale from 1 (least important) to 5 (most important), ensuring the app's design resonated with user needs. The correlations between the "WHATs" (user requirements) and the "HOWs" (technical requirements) were scored using a 3-point ordinal scale, with 1 indicating weak correlation, 5 indicating medium correlation, and 9 indicating the strongest correlation. Results: A total of 13 stakeholders participated in the study, including women who had either underwent or never had health screening, a health administrator, a primary care physician, medical officers, nurses, and software designers. Stakeholder evaluations highlighted cost-free access (mean 4.64, SD 0.81), comprehensive cancer information (mean 4.55, SD 0.69), detailed screening benefits (mean 4.45, SD 0.68), detailed screening facilities (mean 4.45, SD 0.68) and personalized risk calculator for breast and cervical cancers (mean 4.45, SD 0.68) as essential priorities of the app. The highest-ranked features include detailed information on screening procedures (weighted score [WS]=367.84), information on treatment options (WS=345.80), benefits of screening (WS=333.75), information about breast and cervical cancers (WS=332.15), and frequently asked questions about the concerns around screening (WS=312.00). Conclusions: The MAwar app, conceived through a collaborative, stakeholder-driven process, represents a significant step in leveraging digital health solutions to tackle cancer screening disparities. By prioritizing accessibility, information quality, and clarity on benefits, the app promises to encourage early cancer detection and management for targeted communities.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".