Enhancing the reporting of implementation research
Bibliographic record
Abstract
In the 10 years since the inception of Implementation Science, we have witnessed a continued rise in the number of submissions received, reflecting the continued global interest in methods to enhance the uptake of research findings into healthcare practice and policy. We receive over 750 submissions annually, and there is now a large gap between what is submitted and what gets published. In this editorial, we restate the journal scope and current boundaries. We also identify some specific reporting issues that if addressed will help enhance the scientific reporting quality and transparency of the manuscripts we receive. We hope that this editorial acts as a further guide to researchers seeking to publish their work in 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 | 0.538 | 0.867 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.041 | 0.026 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.021 | 0.032 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".