Implementation science: a reappraisal of our journal mission and scope
Bibliographic record
Abstract
The implementation of research findings into healthcare practice has become increasingly recognised as a major priority for researchers, service providers, research funders and policymakers over the past decade. Nine years after its establishment, Implementation Science, an international online open access journal, currently publishes over 150 articles each year. This is fewer than 30% of those submitted for publication. The majority of manuscript rejections occur at the point of initial editorial screening, frequently because we judge them to fall outside of journal scope. There are a number of common reasons as to why manuscripts are rejected on grounds of scope. Furthermore, as the field of implementation research has evolved and our journal submissions have risen, we have, out of necessity, had to become more selective in what we publish. We have also expanded our scope, particularly around patient-mediated and population health interventions, and will monitor the impact of such changes. We hope this editorial on our evolving priorities and common reasons for rejection without peer review will help authors to better judge the relevance of their papers to Implementation Science.
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 | 0.105 | 0.273 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.027 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.014 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".