Expectations and needs of gynecological cancer survivors at the end of primary cancer treatment: A convergent mixed methods study
Bibliographic record
Abstract
Purpose: Gynecological cancer (GC) survivors desire holistic, person-centred, supportive care interventions to address their unmet needs after treatment. The development of such interventions requires an understanding of both the expectations and needs of GC survivors. The purpose of this study was to understand GC survivors’ expectations and needs for post treatment, and to consider how their expectations and needs converge and diverge to inform care. Methods: A convergent mixed-methods design (QUAL+quant) was used. Qualitative data were collected via 1:1 telephone interviews. Quantitative data were collected using the Cancer Survivors’ Unmet Needs measure. Results: Twenty-four individuals participated. Survivors’ expectations for interventions after treatment included the implementation and outcomes of interventions and were grouped into two themes: Wrestling the unknown and Trusted information strengthens capacity. The most common unmet needs were related to existential survivorship. However, mixed-methods analysis revealed participants primarily expected to have their informational needs met in post-treatment interventions. Conclusions: Study findings illuminate GC survivors’ expectations and needs after treatment, and the importance of analyzing both when planning and providing care. Clinicians may use these findings to develop and refine interventions to address unmet needs of GC survivors.
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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".