Insights Into Preferences for Psycho-Oncology Services Among Women With Gynecologic Cancer Following Distress Screening
Bibliographic record
Abstract
Much attention has been given to implementing routine screening programs in cancer care to improve the management of distress following diagnosis. Although patients might screen positive for distress, several studies have found that most then refuse additional psychosocial support. To inform the development of successful models of distress screening, this qualitative study explored preferences for psychosocial care among 18 women diagnosed with a gynecologic cancer who scored at least 4 on the Distress Thermometer (DT). Participants were recruited from a gynecologic oncology outpatient clinic in Newcastle, Australia, and interviewed. Unanimously, participants felt that completing the DT was an integral part of their cancer care. However, half then refused the referral to see a psychologist. These women typically reported that a referral was not needed, because their rating on the DT reflected transient stressors or physical distress. Many also spoke about their need to cope with the challenges they were facing on their own and the extensive social support they already had in place to help them overcome these challenges. In contrast, women who accepted referral to the psychologist often struggled to cope with several losses they felt had existential and long-term effects. Commonly, these women reported not having the social support they needed, managing several concurrent life stressors, and/or not having the repertoire of coping skills they required to "remain afloat." Findings from this study begin to bridge the gap between clinicians' and patients' expectations of how psychosocial services should be used in response to distress screening.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".