Cancer-related concerns and needs among young adults and children on cancer treatment in Tanzania: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Cancer is one of the leading causes of morbidity and mortality worldwide. Seventy percent of deaths of cancer occur in low or middle-income countries, where the resources to provide cancer treatment and care are minimal. Tanzania currently has very inadequate facilities for cancer treatment as there are only five sites, some with limited services; two are in Dar es Salaam and one each in Mwanza, Kilimanjaro and Mbeya that offer cancer treatment. Despite cancer being a prevalent problem in Tanzania, there is a significant shortage of information on the experiences of young people who receive cancer treatment and care. The aim of this study was to explore cancer-related concerns and needs of care and support among young adults and children who are receiving cancer treatment in Dar es Salaam, Tanzania. METHODS: Using an explorative, qualitative design, two focus group discussions (FGDs) with young adults (18 to 25 years) and four FGDs with children (9 to 17 years) were held. Data were transcribed into English and analyzed using content analysis. RESULTS: Identified concerns included physical effects, emotional effects, financial impacts, poor early care, and poor treatment. Identified needs included the need for improved care in hospital by the staff, need for community support, financial needs, needs for improved cancer care and treatment in the hospitals, and the need for increased education about cancer. Resilience was identified, particularly around hope or faith, having hope to be healed, and receiving good care from staff. CONCLUSION: Young adults and children receiving cancer treatment in Tanzania have many needs and concerns. Improvements with regard to the care provided in hospital by the staff, the cancer care and treatment in the hospital, and population-wide education about cancer are necessary to address the identified needs and concerns. Further studies on specific approaches to address the concerns and needs are also warranted.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".