Unrecognized implementation science engagement among health researchers in the USA: a national survey
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
Abstract Background Implementation science (IS) has the potential to serve an important role in encouraging the successful uptake of evidence-based interventions. The current state of IS awareness and engagement among health researchers, however, is relatively unknown. Methods To determine IS awareness and engagement among health researchers, we performed an online survey of health researchers in the USA in 2018. Basic science researchers were excluded from the sample. Engagement in and awareness of IS were measured with multiple questionnaire items that both directly and indirectly ask about IS methods used. Unrecognized IS engagement was defined as participating in research using IS elements and not indicating IS as a research method used. We performed simple logistic regressions and tested multivariable logistic regression models of researcher characteristics as predictors of IS engagement. Results Of the 1767 health researchers who completed the survey, 68% stated they would be able to describe IS. Only 12.7% of the population self-identified as using IS methods. Of the researchers not self-identifying as using IS methods, 86.4% reported using the IS elements “at least some of the time.” Nearly half (47.9%) reported using process/implementation evaluation, 89.2% use IS measures, 27.3% use IS frameworks, and 75.6% investigate or examine ways to integrate interventions into routine health settings. IS awareness significantly reduced the likelihood of all measures of unrecognized IS engagement (aOR 0.13, 95% CI 0.07 to 0.27, p < 0.001). Conclusion Overall, awareness of IS is high among health researchers, yet there is also a high prevalence of unrecognized IS engagement. Efforts are needed to further disseminate what constitutes IS research and increase IS awareness among health researchers.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.083 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.013 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it