Partners, mentors, sponsors & friends: Qualitative analysis of a cervical cancer global health collaboration in Tanzania
Bibliographic record
Abstract
Abstract Background. Though preventable and curable, cervical cancer is among the most common causes of death for women around the world, with the greatest burden in low- and middle-income countries (LMICs). The World Health Organization aims to eliminate cervical cancer by 2030. Aim. Examine the experience of Canadian and Tanzanian clinicians collaborating to implement cervical screening and follow-up care in Tanga, Tanzania, to identify enabling factors. Methods. Iterative qualitative data collection gathered perspectives from Tanzanian focus group participants (n = 8) and Tanzanian and Canadian key informants (n = 3) who have collaborated on the cervical screening program at Tanga Regional Referral Hospital (TRRH), Tanzania. Data collection took a strengths-based approach to examine factors that contribute to the project’s strength and sustainability. Reflexive Thematic Analysis was used to code transcripts and identify themes. Results. Themes of partnership, friendship, mentorship, and sponsorship emerged as qualities that made this Global North-South collaboration strong and sustainable. Participants highlighted the focus on local priorities, trust and reliability, shared decision-making, care for patients and partners, and a genuine interest in learning as enabling factors. Intentional integration into existing clinical structure, budgeting with consideration for community needs, and documentation tools supported their work. Conclusions. Global North-South collaborations can be valuable to enabling the sharing of solutions and establishing systems that function in high-need, low-resource contexts. Fostering international connections and supporting LMIC clinicians to establish cervical screening and follow-up care systems should be prioritized alongside advancing 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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| 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".