Setting Priorities in Childhood Cancer in Low Income Countries Using Nominal Group Technique: Experience from an International Childhood Cancer Forum Exercise in Bangladesh
Bibliographic record
Abstract
Background and Objectives: Cancer is one of the major causes of mortality and morbidity worldwide. The incidence of paediatric cancer in particular, in Bangladesh is alarming and most of these patients die without correct diagnosis and adequate medical treatment (MOHFW, 2008). There is a clear disparity in access to care between rural and urban areas (WHO, 2015; Rahman, 2001). There are no established formal childhood cancer registry systems to help inform planning decisions across the country. Most importantly, there are no explicit priorities or methods for identifying such priorities in Low and Middle Income Countries (LMICs). We used a Nominal Group Technique (NGT) method during the International Childhood Cancer Forum (ICCF) for setting priorities. The following two key objectives were addressed: Trialling the NGT in Bangladesh as a priority setting tool; and identify childhood cancer priorities in Bangladesh. Methods: The Nominal Group Techniques (NGT) method was used to elicit information from the participants of ICCF to identify priorities for research and interventions for childhood cancer care in Bangladesh. Participants were divided into four groups. Each group discussed one question each. Two questions focused on cancer research, and the other two focused on interventions. Results: In regards to outcomes, NGT successfully identified the scale of childhood cancer care and identified priorities/action areas to address in Bangladesh. Six priorities were identified and a successful collaboration for implementation has been established with several international organisations. Conclusion: Nominal group technique was found to be an effective tool to identify research and intervention priorities to address childhood cancer in a developing country. For resource limited countries in similar situations, they could benefit from adopting this approach in healthcare 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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".