Information Needs of Breast Cancer Patients Attending Care at Tikur Anbessa Specialized Hospital: A Descriptive Study
Bibliographic record
Abstract
Purpose: The purpose of this study was to assess the information needs of women with breast cancer attending care at a major hospital in Ethiopia. It also aimed at describing the association of information needs with sociodemographic and clinical variables, preferred sources of information, and time to have it. Patients and Methods: A hospital-based cross-sectional study was conducted on 375 women with breast cancer at Tikur Anbessa Specialized Hospital. Data were collected by interview and Toronto information needs questionnaire for breast cancer which contains 52 items categorized under five domains was pretested, adopted, and used to address the information needs of patients. One way ANOVA was done to get an association of sociodemographic and clinical variables with information needs. All statistical analysis was performed using STATA (Version 14), and statistical significance was set at P ≤ 0.05. Results: The total mean score for overall information needs among breast cancer patients was 238.7 (22.5) with a range scale of 156– 260. Among the five subscales information on disease and information on treatment were the most highly needed areas with a mean percentage of 94.8 and 93.7, respectively; and 254 (67%) of them preferred the information to come from health professionals. Diagnosing as stage IV (p=0.0005) and urban residence (0.02) was associated with less and high information needs, respectively. Conclusion: The information needs of breast cancer patients were high. Determining what the patient’s needs are an important aspect of providing health care especially in cancer care. The healthcare system should include a way of information provision system for breast cancer patients based on their needs. Keywords: breast cancer, information needs, Ethiopia
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".