The Association between Survivorship Care Plans and Patient-Reported Satisfaction and Confidence with Follow-Up Cancer Care Provided by Primary Care Providers
Bibliographic record
Abstract
Survivorship care plans aim to facilitate a smooth transition from tertiary to primary care settings after primary cancer treatment is completed. This study sought to identify the sociodemographic factors associated with receiving a survivorship care plan and examine the relationship between receiving a plan and confidence in follow-up care delivered by primary care providers. A cross-sectional analysis of the Canadian Partnership Against Cancer's Experiences of Cancer Patients in Transition Study was conducted (n = 9970). Separate adjusted multinomial logistic regression models assessed the relationship between survivorship care plans and follow-up care outcomes. Proportion of survivors more likely to receive a survivorship care plan varied by numerous sociodemographic and medical factors, such as cancer type (colorectal and prostate), gender (male), and education (high school or less). In unadjusted and adjusted models, individuals who received a Survivorship Care Plan had significantly higher odds of: having felt their primary care providers were involved; agreeing that their primary care providers understood their needs, knew where to find supports and services, and were able to refer them directly to services; and were confident that their primary care provider could meet their follow-up care needs.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".