Fatalism, Distrust, and Breast Cancer Treatment Refusal in Ghana
Bibliographic record
Abstract
Following recent advancements in science and technology, cancer treatment options have increased remarkably alongside improved survival rates. Yet, some individuals diagnosed with breast cancer refuse treatment. This study aimed to explore how breast cancer patients' personal beliefs and ideas influence their decision to refuse medical treatment. Thirteen participant interviews were selected from a larger cohort for a secondary analysis using the grounded theory approach. The decision to forgo medical treatment was influenced mainly by personal beliefs, which were framed as: 1. Triangle of religion, superstition, and ignorance, 2. Ghanaian traditional belief system, 3. My destiny, 4. Frail patient-staff relationships, 5. Futile appointments, and 6. Endless journey. Together, these fit into two overall themes-fatalism and poor communication patterns between healthcare providers and patients. Personal beliefs and managerial gaps within the health system mainly influence the growing trend of refusal of medical treatment among breast cancer patients in Ghana. These findings highlight the need for breast cancer education, professional counselling, and psychological support services.
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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 | 0.003 | 0.011 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".