Post-treatment health interventions for adult cancer survivors and their family: An integrated review
Bibliographic record
Abstract
Abstract Purpose: This review aimed to aggregate existing literature regarding post-treatment health interventions combined for adult cancer survivors and their families. Methods: An integrative literature review was conducted including quantitative and qualitative studies. The search was carried out in eight databases using the same terms or MESH terms and inclusion of dates from January 2012 to February 2024. After quality assessment, data were extracted and synthesized. The protocol was registered in PROSPERO. Results: Among the seven studies included, two studies were randomised controlled trials, three were observational and two utilized a qualitative approach. The studies originated from France, Australia, Canada, the United Kingdom, and the United States of America. In total, 704 participants were included, of which 294 were cancer survivors, 40 were non-cancer patients, 271 were family and caregivers, and 99 were healthcare professionals. The studies assessed survival durations post-cancer treatment, ranging from 18 months to 6 years. The sparse interventions employed across the studies displayed a multi-faceted approach tailored to address various aspects of cancer survivorship and caregiver support. Conclusion: This review provides insights into the complex landscape of post-treatment support requirements for both cancer survivors and their family caregivers. It underscores the critical necessity for more intervention research in comprehensive, accessible, and support services that address the multifaceted dimensions of survivorship for the patient and family as a unit.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".