Cancer Registration Needs Assessment at a Tertiary Medical Centre in Kilimanjaro, Tanzania
Bibliographic record
Abstract
Cancer burden is increasing in Africa more than in any other continent, but population-based tracking of cancer incidence is incomplete. Cancer registries can improve understanding of cancer incidence. To assess organizational readiness to sustain registry development, we conducted a survey assessing change efficacy, resource availability and change commitment at the Kilimanjaro Christian Medical Centre (KCMC), an academic hospital in Moshi, Tanzania. Fifty-two surveys were returned (80% response rate). There was strong reliability among change efficacy and commitment survey items, with Cronbach's alphas of 0.93 and 0.77, respectively. Clinicians, nurses and administrators conveyed similar responses regarding change efficacy. Clinicians had similar responses for change commitment. Echoing opinion in many low- and middle-income countries, approximately one-third of respondents indicated there were no funds to maintain the registry, and funds were not obtainable. For most resources, respondents felt that resources were sufficient or attainable. Respondents were generally confident and committed to registry implementation. Lessons learned at KCMC may be more broadly relevant.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".