Successful implementation of evidence-based interventions—Factors to be considered
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A range of health behavior interventions demonstrate efficacy in controlled settings, but face challenges when it comes to real-world implementation. These challenges arise due to the variation in participant, implementation staff, and implementation organization needs and resources which influence intervention delivery and effectiveness outcomes of these evidence-based interventions. We present potential approaches and considerations to prevent common pitfalls throughout the process of evidence-based intervention adoption, implementation, and sustainment. This includes using program theory, active engagement, cultural considerations, and understanding the connection between strategies, mechanisms, and outcomes right from the beginning to diligently develop, evaluate, implement, and disseminate evidence-based interventions. These approaches will help behavioral medicine/health psychology implementation researchers to get one step closer to the holy grail: To integrate evidence-based interventions sustainably into programs, systems, policy, and environments to facilitate long-term health behavior change and better health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it