Evidence map›Paper›PMID 42311366›Full record

ArticleCampbell systematic reviews2026

Information Specialist Roles in the Era of Large Language Models: Prompting Continued Professional Development.

Hannah O'Keefe, Claire H Eastaugh, Sheila A Wallace, Fiona R Beyer

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hannah O'KeefeNIHR Innovation Observatory, Newcastle University, Newcastle-upon-Tyne, UK.ORCID https://orcid.org/0000-0002-0107-711X
Claire H EastaughNIHR Innovation Observatory, Newcastle University, Newcastle-upon-Tyne, UK.ORCID https://orcid.org/0000-0002-1371-6601
Sheila A WallaceNIHR Innovation Observatory, Newcastle University, Newcastle-upon-Tyne, UK.ORCID https://orcid.org/0000-0003-2853-3653
Fiona R BeyerNIHR Innovation Observatory, Newcastle University, Newcastle-upon-Tyne, UK.ORCID https://orcid.org/0000-0002-6396-3467

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligenceevidence synthesisinformation retrievalprompt engineering

Identifiers

PMID42311366
PMCPMC13270033

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.