An implementation science primer for psycho-oncology: translating robust evidence into practice
Bibliographic record
Abstract
Abstract Background: It is broadly acknowledged that the next global challenge for psycho-oncology is the implementation of robust evidence-based treatments into routine clinical practice. There is little guidance or texts specific to psycho-oncology to guide researchers and clinicians about implementation science and how to optimally accelerate the translation of evidence into routine practice. This article aims to provide a primer in implementation science for psycho-oncology researchers and clinicians. Methods: We introduce core concepts and principles of implementation science. These include definitions of terms, understanding the quality gap and the need for solid evidence-based interventions. Results: The conceptual models, frameworks, and theories that are used in implementation research are outlined, along with evaluative study designs, implementation strategies, and outcomes. We provide a brief overview of the importance of engaging teams with diverse expertise in research and engaging key stakeholders throughout implementation planning, conduct, and evaluation. The article identifies opportunities to accelerate the implementation of evidence-based psychosocial interventions. Opportunities for greater collaboration across disciplines are highlighted. Examples from psycho-oncology and the broader oncology literature are included to help operationalize concepts. Conclusion: This article describes the fundamental concepts and principles of implementation science for a psycho-oncology audience, to increase the number and quality of implementation studies across the discipline.
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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 | 0.460 | 0.459 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.026 | 0.035 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.029 | 0.066 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".