Quality of life and supportive care needs in prostate cancer: the impact of treatment received and care service utilization among Māori and non-Māori patients in New Zealand
Bibliographic record
Abstract
BACKGROUND: Prostate cancer treatment can lead to significant long-term side effects that impact patients' quality of life and supportive care needs (SCN). This study explores the associations between quality of life (QoL) and SCN among prostate cancer survivors, with a focus on the impact of treatment received, care service utilization, and the differences between Māori and non-Māori patients. METHODS: Random stratified sampling data were collected from 1075 prostate cancer survivors who were diagnosed within the past 5 years. Hierarchical regression analyses examined the associations between QoL domains and SCN, adjusting for demographic, clinical, and treatment-related factors. LASSO (Least Absolute Shrinkage and Selection Operator) was used to select variables to test the interaction effects of different treatments. RESULTS: Significant disparities were found between Māori and non-Māori patients in physical and mental health scores, care service utilization, and overall SCN. Māori men had lower scores in these areas. Most QoL domains were negatively associated with more SCN, particularly mental health and hormonal issues. Androgen deprivation therapy (ADT) exacerbates some negative effects of poor mental health and hormonal issues for non-Māori, while the use of care services and radical prostatectomy (RP) was associated with mitigating SCN for Māori patients. CONCLUSION: This study highlights the complex interplay between QoL, SCN, and treatment modalities among prostate cancer survivors in New Zealand. The findings underscore the need for culturally tailored supportive care services to address the unique needs of Māori 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".