Access to mental health support for rural cancer survivors: a scoping Review
Bibliographic record
Abstract
Objective: This review is an individual master's student-led scoping review. This scoping review will analyze: What factors influence access to mental health services for cancer survivors in rural areas? What strategies are available to improve access to mental health services for cancer survivors living in rural areas? Introduction: The research recognizes different types of cancer can cause or exacerbate serious mental illness (Purushotham et al., 2013). In addition, rural communities suffer from a disproportionate amount of adverse health outcomes (Probst et al., 2019). The primary issue examined are the barriers accessing mental health support for rural cancer survivors. Inclusion Criteria: The review will cover literature on cancer survivors who finished their treatment. The focal concept is accessibility to mental health services for cancer survivors. The setting for the review is rural and underserved areas. Methods: The databases searched were CINAHL Plus (EBSCOhost), MEDLINE (Ovid), Embase (Embase.com), and APA PsycINFO (EBSCOhost). Publications in English and published from 2003 to the present. This scoping review followed the JBI Method protocols. Results: In this dataset, 27 articles were included. A data collection table was created to categorize similar findings which demonstrated the following themes for both research questions. Travel and Transportation, Lack of Education, Community, Financial Challenges, More Research, Better Transitions, Survivorship Care Plans, Rural Locations, Telehealth, and Peer Support. Conclusion: It would be beneficial to investigate the particular mental health support programs available in rural communities and examine the challenges, utilization, and effectiveness of these programs.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".