Age, Health, and Education Determine Supportive Care Needs of Men Younger Than 70 Years With Prostate Cancer
Bibliographic record
Abstract
PURPOSE: It is important to meet the supportive care needs of cancer patients to ensure their satisfaction with their care. A population-wide sample of men younger than 70 years and newly diagnosed with prostate cancer was surveyed to determine their unmet needs in five domains and the factors predicting them. PATIENTS AND METHODS: Eligible men were younger than 70 years and residents in New South Wales, Australia, with newly diagnosed histopathologically confirmed prostate cancer. Sixty-seven percent of eligible men diagnosed between October 2000 and October 2001 participated. Demographic, treatment, and self-reported health data were collected. Information on cancer stage, grade, and prostate-specific antigen was obtained from medical records. Logistic regression analyses determined patient and treatment variables that predicted higher unmet needs. RESULTS: More than half (54%) of men with prostate cancer expressed some level of unmet psychological need, and 47% expressed unmet sexuality needs. Nearly one fourth expressed a moderate or high level of unmet need with respect to changes in sexuality. Sexuality needs were independently predicted by being younger, having had a secondary school education only, having had surgery, and being married, living as married, or divorced. Uncertainty about the future was also an important area of unmet need. CONCLUSION: Attention should be given to sexual and psychological needs in the early months after diagnosis or treatment of prostate cancer, particularly in younger men, those with less education, and those having surgery. Research into better ways of meeting these needs will enable us to meet them with as much rigor as we meet clinical treatment 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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".