Integrating Primary Care Providers through the Seasons of Survivorship
Bibliographic record
Abstract
Traditionally, the role of primary care providers (pcps) across the cancer care trajectory has focused on prevention and early detection. In combination with screening initiatives, new and evolving treatment approaches have contributed to significant improvements in survival in a number of cancer types. For Canadian cancer survivors, the 5-year survival rate is now better than it was a decade ago, and the survivor population is expected to reach 2 million by 2031. Notwithstanding those improvements, many cancer survivors experience late and long-term effects, and comorbid conditions have been noted to be increasing in prevalence for this vulnerable population. In view of those observations, and considering the anticipated shortage of oncology providers, increasing reliance is being placed on the primary care workforce for the provision of survivorship care. Despite the willingness of pcps to engage in that role, further substantial efforts to elucidate the landscape of high-quality, sustainable, and comprehensive survivorship care delivery within primary care are required. The present article offers an overview of the integration of pcps into survivorship care provision. More specifically, it outlines known barriers and potential solutions in five categories: ■ Survivorship care coordination■ Knowledge of survivorship■ pcp-led clinical environments■ Models of survivorship care■ Health policy and organizational advocacy.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".