Evidence map›Paper›PMID 37205132›Full record

ArticleProcedia computer science2023

Audio delivery of health information: An NLP study of information difficulty and bias in listeners.

Arif Ahmed, Gondy Leroy, Han Yu Lu, David Kauchak, Jeff Stone, Philip Harber, Stephen A Rains, Prashant Mishra, Bhumi Chitroda

Abstract read
In one paragraph

Article in Procedia computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Arif AhmedThe University of Arizona, Tucson 85721, U.S.A.
Gondy LeroyThe University of Arizona, Tucson 85721, U.S.A.
Han Yu LuThe University of Arizona, Tucson 85721, U.S.A.
David KauchakPomona College, 333N College Way, Claremont 91711, U.S.A.
Jeff StoneThe University of Arizona, Tucson 85721, U.S.A.
Philip HarberThe University of Arizona, Tucson 85721, U.S.A.
Stephen A RainsThe University of Arizona, Tucson 85721, U.S.A.
Prashant MishraThe University of Arizona, Tucson 85721, U.S.A.
Bhumi ChitrodaThe University of Arizona, Tucson 85721, U.S.A.

Funding

Evidence-based Strategy and Tool to Simplify Text for Patients and ConsumersR01LM011975 · NLM · UNIVERSITY OF ARIZONA · PI LEROY, GONDY · 2015 to 2024
$2.9M
NLM NIH HHS R01 LM011975
6 · The paper itself

Abstract

Health literacy is the ability to understand, process, and obtain health information and make suitable decisions about health care [3]. Traditionally, text has been the main medium for delivering health information. However, virtual assistants are gaining popularity in this digital era; and people increasingly rely on audio and smart speakers for health information. We aim to identify audio/text features that contribute to the difficulty of the information delivered over audio. We are creating a health-related audio corpus. We selected text snippets and calculated seven text features. Then, we converted the text snippets to audio snippets. In a pilot study with Amazon Mechanical Turk (AMT) workers, we measured the perceived and actual difficulty of the audio using the response of multiple choice and free recall questions. We collected demographic information as well as bias about doctors' gender, task preference, and health information preference. Thirteen workers completed thirty audio snippets and related questions. We found a strong correlation between text features lexical chain, and the dependent variables, and multiple choice response, percentage of matching word, percentage of similar word, cosine similarity, and time taken (in seconds). In addition, doctors were generally perceived to be more competent than warm. How warm workers perceive male doctors correlated significantly with perceived difficulty.

Indexed as

audio information deliveryHealth informationHealth literacyNatural Language ProcessingNLPText features

Identifiers

PMID37205132
PMCPMC10191245

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.