A systematic review on interventions to support carers of people with cancer in rural settings
Bibliographic record
Abstract
Objectives/purpose: To identify and examine the effectiveness of interventions that are delivered to support informal caregivers (family, friends, supporters) of people with cancer in rural settings. Methods: Searches were performed in CINAHL, MEDLINE, PsycINFO and Scopus. Primary research studies (quantitative and/or qualitative) that reported on interventions delivered to informal caregivers of people with cancer in rural settings in OECD countries were included. The review adhered to PRISMA guidelines. The protocol was registered on PROSPERO (CRD42023468015). Results: 9,616 articles were identified via searches. After duplicates, 8,314 were screened by title, abstract and full text. Twenty-two articles published between 2013-2022 across a range of geographies (USA n=12; Australia n=6; Canada n=2; Sweden n=1; England n=1) were included. Most studies used a quantitative design (n=10) with some mixed methods (n=8) and others solely qualitative (n=4). Sample sizes ranged from 5-446 informal caregivers. Regular psychosocial support sessions and educational interventions were found to reduce stress, anxiety, and depression for rural caregivers. Learning new communication skills was beneficial to strengthening the dyadic relationship. Interventions involving telehealth reduced financial and travel burden, but digital infrastructure could be barrier in some instances. Conclusion and clinical implications: The needs of informal caregivers need to be urgently recognised as they provide a vital source of support in rural areas where formal cancer services are often lacking.
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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".