Predictors of information needs among women with breast cancer receiving adjuvant therapy at Tikur Anbessa specialized hospital, Addis Ababa Ethiopia: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Women undergoing adjuvant therapy for breast cancer have diverse information needs that remain unfulfilled. Extensive research has shown that access to relevant information about their condition can significantly enhance the quality of life for these women, making it an essential part of cancer care. However, various clinical and socioeconomic factors influence the information needs of these women. Hence, the primary aim of this study is to identify predictors of the information needs of women undergoing adjuvant therapy for breast cancer. In addition, this study will also describe the preferred sources of information and the optimal timing for its acquisition. METHODS: A facility-based cross-sectional study was undertaken at Tikur Anbessa Specialty Hospital, enlisting a cohort comprising 121 women undergoing adjuvant therapy for breast cancer. Trained interviewers administered an Amharic-translated Toronto information needs questionnaire specifically designed for breast cancer to assess the information needs of the study participants Statistical analysis was executed using the sophisticated software SPSS (version 25). Descriptive statistics were employed to summarize the variables of the study. A linear regression analyses was then carried out to identify notable predictors that significantly influenced the information needs of the women. RESULTS: The total mean score for overall information needs in the current study was 194.30 (± 28.01), with a range scale of 142-260 and a standardized mean score of 3.74 (± 0.54). The disease and treatment domains had the highest information needs, with standardized mean scores (standard deviation) of 4.00 (± 0.54) and 3.77 (± 0.59), respectively. 95% of the participants sought information from healthcare professionals, and 67.7% of the women needed the information before beginning the treatments. Predictors of information needs were following a single treatment option (β = 12.68; 95% CI (0.68, 24.68); P = 0.039) and joining higher education and above (β = 17.1; 95% CI (1.47, 34.14); P = 0.033). CONCLUSION: The women exhibited a substantial demand for information. Healthcare professionals need to consider the women's educational background and treatment status while delivering the needed information.
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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 | 0.001 | 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".