Sustainability of Cancer Registration in the Kilimanjaro Region of Tanzania - A Qualitative Assessment
Bibliographic record
Abstract
The projected cancer burden in Africa demands a comprehensive surveillance strategy. Kilimanjaro Christian Medical Centre (KCMC) is developing a population-based cancer registry, and understanding stakeholders' perceptions of factors impacting cancer registration sustainability is critical to its long-term success. We conducted 11 semi-structured qualitative interviews with clinicians and administrators. Interviews were double-coded and evaluated for predetermined and emerging themes. Nearly half (45%) of participants discussed change commitment, stating that the cancer registry would benefit KCMC and that they were committed to it. However, change efficacy was low - participants were not confident in their shared ability to sustain the registry. Most participants (73%) discussed the importance of resource availability and administration support. Several themes emerged across interviews: (i) lack of cancer registry awareness, (ii) ambiguity about its purpose, (iii) the importance of training, (iv) the importance of outcome data, and (v) the importance of international partners. These findings may facilitate cancer registry development and sustainability in similar settings.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".