Adopting Patient-Centred Tools in Cancer Care: Role of Evidence and Other Factors
Bibliographic record
Abstract
Background: Randomized controlled trials (rcts) provide limited evidence to support the use of survivorship care plans (scps), but they provide strong evidence for patient decision aids (ptdas). Despite that evidence, the uptake of ptdas has been limited, but scps are being endorsed and implemented in many cancer programs across Canada. The objective of the present study was to illuminate the decision-making processes involved in the adoption of scps and ptdas. Methods: = 21). Data were collected and analyzed concurrently, using a constant comparative analysis approach. Data collection ended when theoretical saturation was reached. Results: For these types of patient-centred tools, participants noted that high-quality research evidence is often unnecessary for adoption decisions. Six key factors contribute to adoption or non-adoption decisions for scps and ptdas:■ Alignment of research evidence with other evidence■ Perceived clinician benefit■ Endorsement by organizations and professional bodies■ Existence of local champions■ Adaptability to local contexts■ Ability to routinize and reach a large patient population. Conclusions: High-level evidence is not always the main consideration when adopting new tools into practice. And yet, understanding how clinicians and health system decision-makers decide whether and how to adopt new tools is important to optimizing the use of new tools and practices that are supported by research evidence.
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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 | 0.548 | 0.783 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".