Adoption of Patient-Centered Tools by Cancer Care Teams: A Closer Look at Survivorship Care Plans and Patient Decision Aids
Bibliographic record
Abstract
Background: Moving interventions (i.e., new knowledge, tools, and technologies) into clinical practice are often lengthy and challenging processes, even when they are strongly supported by research evidence. Conversely, organizations and providers sometimes adopt interventions in the absence of strong research evidence. Understanding decision-making around the adoption of new interventions is paramount to developing more effective strategies to facilitate the use of evidence-based interventions in practice. Aim: To illuminate the decision-making processes involved in the adoption of patient-centered interventions by cancer care teams, including how research evidence is considered, and identify additional factors influencing these decisions. We focused on two interventions (survivorship care plans [SCPs] and patient decision aids [PtDAs]) due to their differing levels of research evidence and real-world adoption: SCPs = low evidence; high adoption; PtDAs = high evidence; low adoption. Methods: Guided by the principles of grounded theory, we conducted semistructured interviews with clinicians, managers, and administrators of cancer care programs across Canada (n=21). Data were collected and analyzed concurrently, using a constant comparative approach. Data collection ended upon reaching theoretical saturation. Results: Participants emphasized that high-quality research evidence is often unnecessary when making adoption decisions around interventions that are intuitively “good ideas.” Six key factors contributed to adoption/nonadoption decisions around SCPs and PtDAs: 1) alignment (or misalignment) of research evidence with clinical experiences, patient experiences/preferences, and local data; 2) perceived benefit to clinicians themselves; 3) endorsement by respected organizations; 4) existence of local champions; 5) ability to adapt the intervention to local contexts; and 6) ability to routinize the intervention across a large patient population. Conclusion: Many factors influence decisions to adopt patient-centered interventions, including clinicians' experiences and perceived benefits, the existence of organizational and extraorganizational advocates, and ease/reach of implementation.
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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 | 0.056 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".