Survivorship Care Plan (SCPs) use by Rural vs Urban Cancer Survivors: A Qualitative Analysis
Bibliographic record
Abstract
Purpose: With the growing number of cancer survivors, there is an increasing need for transitioning care from cancer centres to primary care providers (PCPs). Survivorship care plans (SCPs) were introduced to aid in this transition. Our study investigated how SCPs were used by survivors living in rural versus urban settings of Eastern Ontario and if there were any differences in needs related to the use of SCPs. Methods: Seventeen urban and 13 rural dwelling survivors who received their SCPs from The Ottawa Hospital Wellness Beyond Cancer Program (WBCP) were recruited. The participants were interviewed about their use of SCPs for 30-45 minutes using an interview guide. Content and thematic analyses were conducted using NVivo-12 to examine differences in how SCPs are used in urban and rural contexts. Results: Survivors from rural communities discuss trusting their PCPs with their transition and viewing SCPs more as a tool for their providers. Survivors living in urban locations report using their SCPs as an advocacy tool to communicate with their PCPs and discuss hesitancy about being discharged from specialist care. Survivors from rural settings report relying on social support networks while their urban counterparts did not report strong social supports. Interestingly, both groups reported the need for more support groups comprised of other survivors. Conclusions: Survivors from rural and urban locations face different challenges that can be addressed by individualizing SCPs. All survivors would benefit from support groups comprised of other survivors. We aim to highlight the specific needs that survivors from different geographical locations may have, to inform SCPs more effectively.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".