Perceptions of care after end-of-treatment among younger women with different gynecologic cancer diagnoses – a qualitative analysis of written responses submitted via a survey
Bibliographic record
Abstract
BACKGROUND: Less attention has been given to younger adults' psycho-oncology care needs than to children and older adults with cancer. The aim was to explore how care following end-of-treatment was perceived by women treated for different gynecologic cancer diagnoses during younger adulthood. METHODS: A sample of 207 women diagnosed with gynecologic cancer 2008 to 2016, aged 19-39 at time of diagnosis answered one open-ended question regarding important aspects of care after end-of-treatment. The written responses were analyzed with manifest content analysis and presented in relation to the women's diagnoses, i.e., cervical (n = 130), ovarian (n = 57), and other gynecologic cancer diagnoses (n = 20). RESULTS: The analysis resulted in three categories: Unmet long-term supportive care needs, Satisfying long-term supportive care, and Health care organizational difficulties. Over half of the women (66.7%) described unmet care needs. The corresponding figures were 80.7, 63.1 and 50% for women diagnosed with ovarian, cervical and other gynecologic cancer diagnoses, respectively. Satisfying supportive care were described by approximately one quarter of the women (26.1%). Among women diagnosed with ovarian cancer 14% described satisfying supportive care. The corresponding figures were 26.9 and 30% for women diagnosed with cervical cancer and other gynecological diagnoses, respectively. Approximately one quarter of the women, irrespectively of diagnosis, described aspects related to health care organizational difficulties (28%). CONCLUSIONS: The results highlight the importance of good quality care linked to the diagnosis and based on an understanding of the woman's need, desire and expectation of support after end-of-treatment.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".