Realist Review of Care Models That Include Primary Care for Adult Childhood Cancer Survivors
Bibliographic record
Abstract
Appropriate models of survivorship care for the growing number of adult survivors of childhood cancer are unclear. We conducted a realist review to describe how models of care that include primary care and relevant resources (eg, tools, training) could be effective for adult survivors of childhood cancer. We first developed an initial program theory based on qualitative literature (studies, commentaries, opinion pieces) and stakeholder consultations. We then reviewed quantitative evidence and consulted stakeholders to refine the program theory and develop and refine context-mechanism-outcome hypotheses regarding how models of care that include primary care could be effective for adult survivors of childhood cancer. Effectiveness for both resources and models is defined by survivors living longer and feeling better through high-value care. Intermediate measures of effectiveness evaluate the extent to which survivors and providers understand the survivor's history, risks, symptoms and problems, health-care needs, and available resources. Thus, the models of care and resources are intended to provide information to survivors and/or primary care providers to enable them to obtain/deliver appropriate care. The variables from our program theory found most consistently in the literature include oncology vs primary care specialty, survivor and provider knowledge, provider comfort treating childhood cancer survivors, communication and coordination between and among providers and survivors, and delivery/receipt of prevention and surveillance of late effects. In turn, these variables were prominent in our context-mechanism-outcome hypotheses. The findings from this realist review can inform future research to improve childhood cancer survivorship care and outcomes.
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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 | 0.014 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".