Translating systematic searches in the APA PsycInfo database from Ovid to EBSCOhost: A tutorial based on a filter translation
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
Search filters are single-concept systematic search strategies created by experts. Filters are a valuable resource for systematic searchers. Typically, filters are designed for a single database in a single interface. If researchers do not have access to that specific interface, the existing filter will be unusable without translation. Filter translation is a complex process that requires an understanding of information retrieval concepts, as well as the unique indexing and search functionality of databases and interfaces. The authors undertook a project to translate an APA PsycInfo search filter for Randomized Controlled Trials/Clinical Controlled Trials (RCT/CCT), developed by Canada's Drug Agency, from the Wolters Kluwer Health Ovid interface to the EBSCO Information Services EBSCOhost interface. We present here a guide for translation, from the first principles of systematic searching to fine details of the relevant database and interfaces, based on our experience and illustrated by a worked example. We discuss each element of a systematic search in a stepwise process, addressing both the underlying information retrieval concepts and the technical strategies for effective translation between the two interfaces. We end with a discussion on translation challenges, with some guidance on how to mitigate potential impacts on sensitivity. While we have endeavored to explain the workings of this process accessibly for researchers who are not experts in systematic searching, anyone undertaking a search translation project should work with a trained information specialist if they lack information retrieval expertise or are unfamiliar with the inner workings of the database, the original interface, and the destination interface.
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.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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