Supportive Care Needs of Women With Gynecologic Cancer
Bibliographic record
Abstract
Gynecologic cancers often place a heavy emotional and physical burden on patients. However, there is a lack of information about the types of supportive care needs that these patients have, the services that are available, and whether patients want help with their needs. The aims of this cross-sectional, descriptive study were to (1) identify the supportive care needs (physical, emotional, social, spiritual, psychological, informational, and practical) of women with gynecologic cancer who attended a comprehensive, outpatient cancer center in Ontario, Canada, and (2) determine if patients wanted assistance in meeting those needs. A total of 103 patients participated in this study by completing a self-report questionnaire. Sixty-five of the women were no longer on treatment at the time of completing the survey. Eight of the top 10 most frequently reported needs were non physical, such as fears about the cancer returning or spreading. The data indicated that a range of needs remained unmet for this patient group. However, identifying the presence of a need did not necessarily mean that a patient wanted to have assistance with the need. Suggestions for practice and future research are offered to assist healthcare professionals in providing care to these patients.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".