What is known about the role of rural-urban residency in relation to self-management in people affected by cancer who have completed primary treatment? A scoping review
Bibliographic record
Abstract
PURPOSE: Despite wide acknowledgement of differences in levels of support and health outcomes between urban and rural areas, there is a lack of research that explicitly examines these differences in relation to self-management in people affected by cancer following treatment. This scoping review aimed to map the existing literature that examines self-management in people affected by cancer who were post-treatment from rural and urban areas. METHODS: Arksey and O'Malley's framework for conducting a scoping review was utilised. Keyword searches were performed in the following: Academic Search Complete, CINAHL, MEDLINE, PsycINFO, Scopus and Web of Science. Supplementary searching activities were also conducted. RESULTS: A total of 438 articles were initially retrieved and 249 duplicates removed leaving 192 articles that were screened by title, abstract and full text. Nine met the eligibility criteria and were included in the review. They were published from 2011 to 2018 and conducted in the USA (n = 6), Australia (n = 2) and Canada (n = 1). None of the studies offered insight into self-managing cancer within a rural-urban context in the UK. Studies used qualitative (n = 4), mixed methods (n = 4) and quantitative designs (n = 1). CONCLUSION: If rural and urban populations define their health in different ways as some of the extant literature suggests, then efforts to support self-management in both populations will need to be better informed by robust evidence given the increasing focus on patient-centred care. It is important to consider if residency can be a predictor of as well as a barrier or facilitator to self-management.
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 | 0.014 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".