The development and implementation of the After Cancer Treatment Transition (ACTT) Program for survivors of cancer
Bibliographic record
Abstract
Background: The After Cancer Treatment Transition (ACTT) program at Women's College Hospital (Toronto) is a transitional follow-up program for patients, their families, and healthcare providers to address the broad range of post-cancer treatment and survivorship needs. This publication describes the systematic development and implementation of the ACTT program, with a focus on the advanced practice nursing (APN) role. Program Development: ACTT development required the collaboration of an APN, a general practitioner in oncology (GPO), and an inter-professional team. ACTT developers proposed a clinic structure in an ambulatory setting, linking healthcare professionals to provide post-treatment follow-up and ongoing survivorship care. Post-treatment guidelines were developed based on expert oncologist consensus, cancer site group input, and evidence-informed guidelines or best practice recommendations. Program Implementation: Initial challenges and concerns were rooted in the requirements that post-cancer treatment care was maintained and survivor needs were addressed. Cancer site groups and the inter-professional teams provided continuous feedback on processes and protocols. ACTT established a standard approach to transition patients safely and effectively out of tertiary care and, ultimately, to primary care. Current ACTT Program: ACTT delivers comprehensive posttreatment and survivorship care through close collaboration between the GPO and APN. Both roles specialize in managing late or persistent effects, cancer surveillance and prevention, and addressing psychosocial needs prior to discharge to primary care. The survivorship care plan provided by ACTT is an informative tool for both patient and primary care provider to continue post-treatment follow-ups. Future Directions: Next steps for ACTT include expanding to other cancer specialties, exploring new ways to deliver care, optimizing the transition of care, and conducting comprehensive evaluations of patient reported 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 | 0.038 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".