“Some They Need Male, Some They Need Female”: A Gendered Approach for Breast Cancer Detection in Uganda
Bibliographic record
Abstract
Introduction: There are several challenges associated with breast cancer detection in Uganda and other low-and-middle-income countries. One of the identified challenges is attributed to the health workers' gender, which facilitates gender disparities in access to breast cancer detection services. Although this challenge is well acknowledged in existing literature, there are hardly any studies on how it can be addressed. Therefore, drawing on an intersectionality lens, our study examined how to address gender disparities facilitated by health workers' gender in accessing breast cancer detection services in Uganda. Materials and Methods: We collected qualitative data through semi-structured interviews with twenty participants comprising community health workers, primary health care practitioners, non-governmental organizations, district health team, and the Ministry of Health. For the data analysis, thematic analysis was conducted on NVivo using Braun and Clarke's non-linear 6-step process to identify the themes presented in the results section. Results: Four themes emerged from the data analysis: understanding a woman's gender constructions; health workers' approachability; focus on professionalism, not sex; and change in organizational culture. These themes revealed participants' perceptions regarding how to address gender disparities relating to the role health workers' gender play in breast cancer detection. Through the intersectionality lens, our findings showed how gender intersects with other social stratifiers such as religious beliefs, familial control, health worker's approachability, and professionalism within the health workforce. Conclusion: Our findings show that the solutions to address gender disparities in breast cancer detection are individually and socially constructed. As such, we recommend a gendered approach to understand and redress the underlying power relations perpetuating such constructions. We conclude that taking a gendered approach will ensure that breast cancer detection programs are context-appropriate, cognizant of the prevailing cultural norms, and do not restrict women's access to breast cancer detection services.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".