An Examination of Systematic Reviews in the Engineering Literature
Bibliographic record
Abstract
Abstract Systematic reviews are a well-established method of research synthesis in medicine and the clinical sciences. Their use in other disciplines has been growing, especially in areas that collaborate with the health sciences. At the authors' institution, requests for help with systematic reviews have become more frequent in recent years across several non-health-science fields. In this paper, the authors explore the use of systematic reviews in the engineering literature, and the need for engineering librarians to be familiar with the conventions of this methodology. This study seeks to answer three questions: 1) are systematic reviews being published in the engineering literature more frequently? 2) is this methodology more prevalent in certain engineering disciplines than in others? and 3) do systematic reviews see greater use than other types of papers? First, the share of papers using this methodology is examined to confirm the authors' impression that the use of this methodology has increased beyond the rate of increase in publications overall. Next, bibliographic records from several abstracting and indexing databases are analyzed to identify the subject areas within engineering in which research synthesis techniques are most prevalent. Citation counts are also analyzed to determine whether systematic reviews are more likely to be used than other papers in the same subject areas, as has been shown to occur in some non-engineering disciplines. Finally, options for librarians to support this type of research synthesis are discussed, including building familiarity with tools such as Rayyan and Covidence, offering expert search guidance through instruction and consultation, and co-authorship.
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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 | 0.236 | 0.608 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.082 | 0.070 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".