ArticleCampbell systematic reviews2026
Information Specialist Roles in the Era of Large Language Models: Prompting Continued Professional Development.
Article in Campbell systematic reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Expanding the Scope of Prompt Engineering Training for Information Specialists in Evidence Synthesis.Campbell systematic reviews · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prompt engineering is the formation of queries or instructions (prompts) that are deployed in large language models. These prompts are often underscored by frameworks, designed to give structure and encourage robust answers. Discussions in recent information specialists' networks and events have highlighted on multiple occasions that information specialists are well placed to undertake prompt engineering tasks. However, there is little published information outlining why and how information specialists are best placed for these tasks and the universal understanding between information specialists has not filtered out to the wider research synthesis community so progress in this area is slow. Here, we discuss the parallels between information specialist tasks and large language model engineering tasks and demonstrate that the parallels run deeper than just prompts. There are strong similarities between information retrieval and context engineering, prompt engineering and vibing. In the briefest sense, we can consider context engineering to be like a search platform, prompt engineering like a structured search strategy, and vibe coding like a search engine input. Knowledge sharing and dissemination of these core concepts amongst information specialists and research synthesists will drive methods development, particularly with the rise of large language models in synthesis automation, give potential for continual professional development courses and e-learning to be developed, and expand the roles of information specialists. To initiate progress in this area, we discuss the anticipated future direction of information specialist roles.
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Registered trials
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