Evidence map›Paper›PMID 23903235›Full record

ArticleJournal of medical Internet research2013

User evaluation of the effects of a text simplification algorithm using term familiarity on perception, understanding, learning, and information retention.

Gondy Leroy, James E Endicott, David Kauchak, Obay Mouradi, Melissa Just

Abstract readEvaluation Study
In one paragraph

Article in Journal of medical Internet research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Text and Audio Simplification: Human vs. ChatGPT.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024
    Article
  5. Article
  6. A survey of automated methods for biomedical text simplification.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  7. Article
  8. Article
  9. Article
  10. Predicting Transition Words Between Sentence for English and Spanish Medical Text.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019
    Article
  11. Article
  12. Article
  13. Measuring Text Difficulty Using Parse-Tree Frequency.Journal of the Association for Information Science and Technology · 2017
    Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
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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

5 authors.

Gondy LeroyInformation Systems and Technology, Claremont Graduate University, Claremont, CA 91711, United States. gondy.leroy@cgu.edu
James E Endicott
David Kauchak
Obay Mouradi
Melissa Just

Funding

Large-scale evaluation of text features affecting perceived and actual text diffiR03LM010902 · NLM · UNIVERSITY OF ARIZONA · PI LEROY, GONDY · 2011 to 2012
$131k
NLM NIH HHS 1R03LM010902-01NLM NIH HHS R03 LM010902
6 · The paper itself

Abstract

backgroundAdequate health literacy is important for people to maintain good health and manage diseases and injuries. Educational text, either retrieved from the Internet or provided by a doctor's office, is a popular method to communicate health-related information. Unfortunately, it is difficult to write text that is easy to understand, and existing approaches, mostly the application of readability formulas, have not convincingly been shown to reduce the difficulty of text.

objectiveTo develop an evidence-based writer support tool to improve perceived and actual text difficulty. To this end, we are developing and testing algorithms that automatically identify difficult sections in text and provide appropriate, easier alternatives; algorithms that effectively reduce text difficulty will be included in the support tool. This work describes the user evaluation with an independent writer of an automated simplification algorithm using term familiarity.

methodsTerm familiarity indicates how easy words are for readers and is estimated using term frequencies in the Google Web Corpus. Unfamiliar words are algorithmically identified and tagged for potential replacement. Easier alternatives consisting of synonyms, hypernyms, definitions, and semantic types are extracted from WordNet, the Unified Medical Language System (UMLS), and Wiktionary and ranked for a writer to choose from to simplify the text. We conducted a controlled user study with a representative writer who used our simplification algorithm to simplify texts. We tested the impact with representative consumers. The key independent variable of our study is lexical simplification, and we measured its effect on both perceived and actual text difficulty. Participants were recruited from Amazon's Mechanical Turk website. Perceived difficulty was measured with 1 metric, a 5-point Likert scale. Actual difficulty was measured with 3 metrics: 5 multiple-choice questions alongside each text to measure understanding, 7 multiple-choice questions without the text for learning, and 2 free recall questions for information retention.

resultsNinety-nine participants completed the study. We found strong beneficial effects on both perceived and actual difficulty. After simplification, the text was perceived as simpler (P<.001) with simplified text scoring 2.3 and original text 3.2 on the 5-point Likert scale (score 1: easiest). It also led to better understanding of the text (P<.001) with 11% more correct answers with simplified text (63% correct) compared to the original (52% correct). There was more learning with 18% more correct answers after reading simplified text compared to 9% more correct answers after reading the original text (P=.003). There was no significant effect on free recall.

conclusionsTerm familiarity is a valuable feature in simplifying text. Although the topic of the text influences the effect size, the results were convincing and consistent.

Indexed as

AlgorithmsInformation ServicesAdultAgedFemaleHumansMaleMiddle AgedUnified Medical Language SystemWritingYoung Adultconsumer health informationevaluation studyhealth literacynatural language processingtext simplification

Identifiers

PMID23903235
PMCPMC3742397

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.