Identifying supportive care needs of women with ovarian cancer
Bibliographic record
Abstract
Women diagnosed with ovarian cancer may experience many shortterm and long-term effects from cancer and its treatment. Cancer has more than a physical impact, yet there is a lack of information about the types of needs these women have and whether they want help in meeting their needs. The main purpose of this cross-sectional, descriptive study was to identify the supportive care needs (physical, emotional, social, informational, spiritual, psychological and practical) of women with ovarian cancer who attended a comprehensive, outpatient cancer centre. A further purpose was to determine if women wanted assistance in meeting those needs. A total of 50 women diagnosed with ovarian cancer participated in this study by completing a self-report questionnaire (The Supportive Care Needs Survey). The data indicated that a range of supportive care needs remained unmet for this patient group. Eight of the top 10 most frequently reported needs were psychosocial, such as fears about the cancer returning or spreading. The women also expressed a range of difficulty in managing their needs. However, despite this reality, significant numbers of women indicated they did not wish to have assistance from the clinic staff with some needs. Suggestions for practice and future research are offered to assist oncology nurses in providing care to these women.
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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 | 0.004 |
| 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 | 0.001 |
| 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".