Considerations for multimodal prehabilitation in women with gynaecological cancers: a scoping review using realist principles
Bibliographic record
Abstract
Abstract Background There is increasing recognition that prehabilitation is important as a means of preparing patients physically and psychologically for cancer treatment. However, little is understood about the role and optimal nature of prehabilitation for gynaecological cancer patients, who usually face extensive and life-changing surgery in addition to other treatments that impact significantly on physiological and psychosexual wellbeing. Review question This scoping review was conducted to collate the research evidence on multimodal prehabilitation in gynaecological cancers and the related barriers and facilitators to engagement and delivery that should be considered when designing a prehabilitation intervention for this group of women. Methods Seven medical databases and four grey literature repositories were searched from database inception to September 2021. All articles, reporting on multimodal prehabilitation in gynaecological cancers were included in the final review, whether qualitative, quantitative or mixed-methods. Qualitative studies on unimodal interventions were also included, as these were thought to be more likely to include information about barriers and facilitators which could also be relevant to multimodal interventions. A realist framework of context, mechanism and outcome was used to assist interpretation of findings. Results In total, 24 studies were included in the final review. The studies included the following tumour groups: ovarian only (n = 12), endometrial only (n = 1), mixed ovarian, endometrial, vulvar (n = 5) and non-specific gynaecological tumours (n = 6). There was considerable variation across studies in terms of screening for prehabilitation, delivery of prehabilitation and outcome measures. Key mechanisms and contexts influencing engagement with prehabilitation can be summarised as: (1) The role of healthcare professionals and organisations (2) Patients’ perceptions of acceptability (3) Factors influencing patient motivation (4) Prehabilitation as a priority (5) Access to prehabilitation. Implications for practice A standardised and well evidenced prehabilitation programme for women with gynaecological cancer does not yet exist. Healthcare organisations and researchers should take into account the enablers and barriers to effective engagement by healthcare professionals and by patients, when designing and evaluating prehabilitation for gynaecological cancer patients.
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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 | 0.027 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".