Predictors of Supportive Care in Young Adults Diagnosed With a Gynaecologic Malignancy
Bibliographic record
Abstract
OBJECTIVE: In Canada, gynaecologic cancers significantly impact adults aged 18-40 years, who may undergo multiple treatment modalities impacting their overall well-being. The objective of this study was to understand the individual characteristics, supportive care, and informational needs of these persons. METHODS: A cross-sectional survey of N = 50 adults (aged ≤40 years) receiving treatment (chemotherapy, surgery, and/or radiation) or under surveillance for gynaecologic cancer at a tertiary cancer centre in Toronto, Canada. RESULTS: Unmet supportive care needs were commonly reported, with more than half of the participants indicating unmet needs in all but 1 of the 5 domains (psychological [78%], health system and information [68%], physical and daily living [54%], sexuality [50%], and patient care and support [46%]). Unmet supportive care needs were associated with a host of psychosocial, demographic, and clinical factors. Notably, for psychosocial factors, low resilience was associated with a higher likelihood of unmet supportive care needs (health system and information [OR 2.97, 95% CI 1.06-8.35], physical and daily living [4.95, 1.69-22.66], and patient care and support [5.91, 1.77-40.50] domains and low perceived information and satisfaction [3.11, 1.30-11.60]). Various other socio-demographic (e.g., non-European cultural origins and other ethnicity, further distance to cancer centre) and clinical factors (e.g., number of treatment modalities) were also related to unmet needs. CONCLUSION: Future studies must examine how to best meet the needs of younger adults affected by gynaecologic cancers to improve client-centred, supportive care through early intervention and adequate resources.
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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 | 0.000 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".