Access to Medical and Supportive Care for Rural and Remote Cancer Survivors in Northern British Columbia
Bibliographic record
Abstract
BACKGROUND: Rural cancer survivors (RCS) potentially have unique medical and supportive care experiences when they return to their communities posttreatment because of the availability and accessibility of health services. However, there is a limited understanding of cancer survivorship in rural communities. PURPOSE: The purpose of this study is to describe RCS experiences accessing medical and supportive care postcancer treatment. METHODS: Interviews and focus groups were conducted with 52 RCS residing in northern British Columbia, Canada. The data were analyzed using qualitative content analysis methods. RESULTS: General Population RCS and First Nations RCS experienced challenges accessing timely medical care close to home, resulting in unmet medical needs. Emotional support services were rarely available, and, if they did exist, were difficult to access or not tailored to cancer survivors. Travel and distance were barriers to medical and psychological support and services, not only in terms of the cost of travel, but also the toll this took on family members. Many of the RCS lacked access to trusted and useful information. Financial assistance, for follow-up care and rehabilitation services, was rarely available, as was appropriate employment assistance. CONCLUSION: Medical and supportive care can be inaccessible, unavailable, and unaffordable for cancer survivors living in rural northern communities.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".