Education and training for implementation science: our interest in manuscripts describing education and training materials
Bibliographic record
Abstract
Alongside the growth in interest in implementation science, there has been a marked increase in training programs, educational courses, degrees, and other offerings in implementation research and practice to meet the demand for this expertise. We believe that the science of capacity building has matured but that we can advance it further by shining light on excellent work in this area and by highlighting gaps for future research. At Implementation Science, we regularly receive manuscripts that describe or evaluate training materials, competencies, and competency development in implementation curricula. We are announcing a renewed interest in manuscripts in this area, with specifications described below.
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.
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 | 0.180 | 0.598 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.030 | 0.018 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.022 | 0.022 |
| Insufficient payload (model declined to judge) | 0.038 | 0.025 |
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".