Relevant Journals for Identifying Implementation Science Articles: Results of an International Implementation Science Expert Survey
Bibliographic record
Abstract
In implementation science (IS), conducting well-targeted and reproducible literature searches is challenging due to non-specific and varying terminology that is fragmented over multiple disciplines. A list of journals that publish IS-relevant content for use in search strings can support this process. We conducted a cross-sectional online survey of 56 Australian, European, and North American IS experts to identify and prioritize relevant journals that publish IS articles. Journals' relevance was assessed by providing each with a list of 12 journals, to which they were encouraged to add additional journal names and comments as free text. We also assessed which journals had published special IS-focused issues—identified via PubMed and Google searches—over the last 20 years. Data were analyzed descriptively. Between February 28 and March 15, 2020, a purposive sample of 34/56 experts participated in the survey (response rate: 60.7%). Implementation Science and BMC Health Services Research were perceived as relevant by 97.1% of participants; other journals' relevance varied internationally. Experts proposed 50 additional journals from various clinical fields and health science disciplines. We identified 12 calls and 53 special issues on IS published within various journals and research fields. Experts' comments confirmed the described challenges in identifying IS literature. This report presents experts' ratings of IS journals, which can be included in strategies supporting searches of IS evidence. However, challenges in identifying IS evidence remain geographically and interdisciplinary. Further investment is needed to develop reproducible search strings to capture IS evidence as an important step in improving IS research quality.
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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 | 0.115 | 0.280 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".