Optimizing Adherence to Oral Anticancer Agents: Results from an Implementation Mapping Study
Bibliographic record
Abstract
Clinical trials inform cancer care, yet real-world outcomes often diverge due to patient-related factors, like age, organ dysfunction, and nonadherence to oral anticancer agents (OAAs). While oncology organizations emphasize patient support programs, practical guidance on designing and implementing these programs is limited. We conducted a two-phase, mixed-methods study to enhance the adoption, implementation, and sustainability of an OAA adherence program (OAP). In phase 1, we used implementation mapping (IM) with a multidisciplinary expert panel to develop six strategies: (1) memorandum of understanding (MOU), (2) data-driven presentation, (3) standard operating procedures (SOPs), (4) motivational interviewing (MI) training, (5) electronic health record (EHR) templates, and (6) key performance indicators (KPIs). In phase 2, oncology professionals (n = 34) completed surveys, and a subset (n = 10) participated in interviews to assess feasibility, acceptability, and appropriateness. EHR templates and SOPs were rated as the most feasible and acceptable strategies, while MI training and formal agreements received moderate ratings. Interviews highlighted the importance of leadership buy-in, incremental implementation, and clear documentation. Participants valued KPIs for tracking adherence and outcomes but noted resource constraints and staff workload as challenges. Using IM, we co-developed strategies to activate OAA adherence-focused clinical programs. Tools standardizing care, like EHR templates and SOPs, were highly endorsed. Future work will test these strategies in a hybrid trial to improve real-world oncology 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 | 0.153 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".