Evidence map›Paper›PMID 36787164›Full record

ArticleJMIR formative research2023

Multiple Automated Health Literacy Assessments of Written Health Information: Development of the SHeLL (Sydney Health Literacy Lab) Health Literacy Editor v1.

Julie Ayre, Carissa Bonner, Danielle M Muscat, Adam G Dunn, Eliza Harrison, Jason Dalmazzo, Dana Mouwad, Parisa Aslani, Heather L Shepherd, Kirsten J McCaffery

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 1 pooled it
24.9field-weighted citation impact, top 1% of its field
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

37 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.

  1. Pooled it
  2. Trial
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  5. Trial
  6. Communicating the Imperfect Diagnostic Accuracy of COVID-19 Rapid Antigen Self-Tests: An Online Randomized Experiment.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Trial
  7. Article
  8. Article
  9. Article
  10. Building the Capacity of Adolescents as Researchers: The Co-Creation of the Health Hive Online Course.Health expectations : an international journal of public participation in health care and health policy · 2026
    Article
  11. Article
  12. Article
  13. Article
  14. Article
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  16. Article
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  19. Understanding Patient Rights: A Pilot Study Assessing Health Literacy in Written Pre-Appointment Letters.International journal of environmental research and public health · 2025
    Article
  20. 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

10 authors at 2 institutions in 1 country.

Julie AyreSydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0002-5279-5189
Carissa BonnerSydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0002-4797-6460
Danielle M MuscatSydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0001-6106-6298
Adam G DunnBiomedical Informatics and Digital Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0002-1720-8209
Eliza HarrisonBiomedical Informatics and Digital Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0002-7290-8187
Jason DalmazzoBiomedical Informatics and Digital Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0003-4482-248X
Dana MouwadWestern Sydney Local Health District, Health Literacy Hub, Sydney, Australia.ORCID https://orcid.org/0000-0002-0393-0452
Parisa AslaniSchool of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0002-7976-8236
Heather L ShepherdSusan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0002-3350-1325
Kirsten J McCafferySydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0003-2696-5006
The University of Sydney · AUSydney Local Health District · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Producing health information that people can easily understand is challenging and time-consuming. Existing guidance is often subjective and lacks specificity. With advances in software that reads and analyzes text, there is an opportunity to develop tools that provide objective, specific, and automated guidance on the complexity of health information. This paper outlines the development of the SHeLL (Sydney Health Literacy Lab) Health Literacy Editor, an automated tool to facilitate the implementation of health literacy guidelines for the production of easy-to-read written health information. Target users were any person or organization that develops consumer-facing education materials, with or without prior experience with health literacy concepts. Anticipated users included health professionals, staff, and government and nongovernment agencies. To develop this tool, existing health literacy and relevant writing guidelines were collated. Items amenable to programmable automated assessment were incorporated into the Editor. A set of natural language processing methods were also adapted for use in the SHeLL Editor, though the approach was primarily procedural (rule-based). As a result of this process, the Editor comprises 6 assessments: readability (school grade reading score calculated using the Simple Measure of Gobbledygook (SMOG)), complex language (percentage of the text that contains public health thesaurus entries, words that are uncommon in English, or acronyms), passive voice, text structure (eg, use of long paragraphs), lexical density and diversity, and person-centered language. These are presented as global scores, with additional, more specific feedback flagged in the text itself. Feedback is provided in real-time so that users can iteratively revise and improve the text. The design also includes a "text preparation" mode, which allows users to quickly make adjustments to ensure accurate calculation of readability. A hierarchy of assessments also helps users prioritize the most important feedback. Lastly, the Editor has a function that exports the analysis and revised text. The SHeLL Health Literacy Editor is a new tool that can help improve the quality and safety of written health information. It provides objective, immediate feedback on a range of factors, complementing readability with other less widely used but important objective assessments such as complex and person-centered language. It can be used as a scalable intervention to support the uptake of health literacy guidelines by health services and providers of health information. This early prototype can be further refined by expanding the thesaurus and leveraging new machine learning methods for assessing the complexity of the written text. User-testing with health professionals is needed before evaluating the Editor's ability to improve the health literacy of written health information and evaluating its implementation into existing Australian health services.

Indexed as

comprehensionhealth communicationhealth educationhealth literacymedicine informationreadability

Identifiers

PMID36787164
PMCPMC9975914
OpenAlexW4320710158

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.