The Suitability of the Health Belief Model as an Assessment Framework for Women With Breast Ill-Health
Bibliographic record
Abstract
Objectives: Globally, breast cancer is the commonest cancer in women. Empirical literature indicate that it is the second cause of cancer-related mortality in high-resource regions, while it is the most common cause of cancer-related deaths among women in poor-resource regions. This study presents the suitability of the health belief model (HBM) as a framework for carrying out a comprehensive assessment of women with late-stage breast cancer in Nigeria. Materials and Methods: This qualitative study employed interpretive description as its methodological approach, while the HBM was the conceptual framework. Two institutional review boards granted approval to conduct the study. Thirty women with advanced breast cancer were recruited for the study using purposeful sampling techniques. Components of the original HBM were identified to carry out the investigation. Data analysis was inductive. Results: Findings indicated that the participants viewed breast cancer as a definite threat- both as a spiritual attack – an arrow shot by the enemy, and as a killer disease. Many of their perceptions appeared to be culturally based, while others were based on their individual experiences. They perceived some benefits to both traditional and medical treatment options. Conclusions: Interventions that address people’s cultural and individual perceptions enables a comprehensive assessment of the patients with breast cancer, which can improve the treatment outcomes and survival rates of disease.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".