Enhancing cultural sensitivity in the implementation of the Fertility Quality of Life Tool in Sudan: a science diplomacy perspective
Bibliographic record
Abstract
Background: Infertility is a global health challenge impacting quality of life, particularly in low and middle-income countries such as Sudan. The Fertility Quality of Life (FertiQoL) tool, a standardized questionnaire, is pivotal in assessing fertility-related quality of life. However, existing research on its utility has primarily been conducted in Global North and High-Income Countries, highlighting the need to shift away from neocolonialism to promote truly inclusive research and effective healthcare practices. Science diplomacy, through the adaptation and culturally sensitive implementation of research tools, can serve as a catalyst for addressing health disparities on a global scale. This study aims to assess methodological and cultural considerations that impact the implementation of the FertiQoL tool in Sudan, framed within the context of science diplomacy and neocolonialism. By investigating the challenges and opportunities of utilizing this tool in a non-Western cultural setting, we seek to contribute to the broader discussion on decolonizing global health research. Methods: Utilizing an explanatory sequential design involving surveys and interviews, we conducted a study in a Sudanese fertility clinic from November 2017 to May 2018. A total of 102 participants were recruited using convenience sampling, providing socio-demographic, medical, and reproductive history data. The Arabic version of FertiQoL was administered, with 20 participants interviewed and 82 surveyed (40 self-administered and 42 provider-administered). We applied descriptive statistics, one-way ANOVA, thematic analysis, and triangulation to explore methodological and cultural nuances. Results: = 0.03)], qualitative insights unveiled vital cultural considerations. Interpretation challenges related to concepts like hope and jealousy emerged during interviews. Notably, the social domain of FertiQoL was found to inadequately capture the social pressures experienced by infertile individuals in Sudan, underscoring the importance of region-specific research. Despite these challenges, participants perceived FertiQoL as a comprehensive and valuable tool with broader utility beyond assessing fertility-related quality of life. Conclusion: Our findings emphasize the significance of incorporating cultural sensitivity into the interpretation of FertiQoL scores when implementing it globally. This approach aligns with the principles of science diplomacy and challenges neocolonial structures by acknowledging the unique lived experiences of local populations. By fostering cross-cultural understanding and inclusivity in research, we can enhance the implementation of FertiQoL and pave the way for novel interventions, increased funding, and policy developments in the Global South, ultimately promoting equitable global health.
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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 | 0.071 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".