Information Needs of Breast Cancer Patients at Cancer Diseases Hospital, Lusaka, Zambia
Bibliographic record
Abstract
Background: Breast cancer is the second most common cancer worldwide and the second most common among Zambian women. Breast cancer diagnosis being a stressful experience, causes psychological and emotional disruption that can be abated by meeting information needs of the affected patients. In light of the escalating cases of Breast cancer among the Zambian women, the study examined a special aspect of cancer management which is usually neglected in most cases. Aim: The main objective of the study was to assess information needs of breast cancer patients at the Cancer Diseases Hospital in Lusaka, Zambia using a modified structured interview schedule adopted from the Toronto Information Needs Questionnaire-Breast Cancer (TINQ-BC). Methods: A descriptive cross-sectional design was used to elicit the information needs of breast cancer patients. One hundred and ten (97% response rate) participants were selected using simple random sampling method and data was collected using a modified structured interview schedule adopted from the Toronto Information Needs Questionnaire-Breast Cancer (TINQ-BC). Stata 10.0 (StataCorp, 2008) was employed for all quantitative data analysis and graphical presentation of data. Results: The overall score for information needs was obtained by adding the scores across all the five information needs categories which were further divided into three categories namely: low important scores, of less than 50%, moderately important scores of 50% - 70% and highly important scores ranged above 70% of the 200 total scores. Out of the 110 participants recruited, 88 (80%) indicated that the information across the five categories was moderately important. Logistic regression of information needs and posited determinants revealed that anxiety levels; education level; presence of co-morbidity; and being on treatment were significant determinants of patients’ informational needs (Effect’s p ≤ 0.05). Conclusion: The findings of this study support the idea that breast cancer patients are seeking more information on their illness, hence information provision is one of the most important factors for providing high quality cancer care across the whole cancer continuum. Therefore, appreciating the information needs of breast cancer patients is substantial in improving care.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".