Prevalence, intensity, and predictors of the supportive care needs of women diagnosed with breast cancer: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The assessment of supportive care needs is a crucial step in the development of appropriate interventions that may improve the quality of life of cancer patients. This review describes and analyzes the prevalence and predictors of the unmet supportive care needs of breast cancer (BC) patients and survivors and suggests paths for further research. METHOD: Multiple databases were searched, considering only quantitative studies using validated needs assessment instruments and focusing uniquely on women diagnosed with BC. RESULTS: Out of 761 hits, 23 studies answered to all eligibility criteria. Nineteen were cross-sectional, and the remaining four were longitudinal. Most included patients at different moments along the BC trajectory, from diagnosis to decades into survivorship, with the major proportion of patients under treatment. Only five concentrated on the posttreatment phase into extended survivorship. The concerns of women diagnosed with BC clustered around psychological and information needs, with the top concern being 'fear of the cancer returning'. Predictors of higher levels of needs included advanced disease stage, greater symptom burden, shorter time since diagnosis, higher levels of distress, and younger age. Prevalence differed between cultures with Asian women reporting greater information needs and lower psychological needs compared with Western women. CONCLUSIONS: Revealing which needs BC patients consider most urgent and the factors related to greater needs will permit the development of improved and targeted supportive care. Future research should comprise longitudinal designs concentrating on women at specific moments along the BC trajectory for a dynamic understanding of these needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it