Dataset Information

The Appropriateness of Language Found in Research Consent Form Templates: A Computational Linguistic Analysis.

ABSTRACT:

Background

To facilitate informed consent, consent forms should use language below the grade eight level. Research Ethics Boards (REBs) provide consent form templates to facilitate this goal. Templates with inappropriate language could promote consent forms that participants find difficult to understand. However, a linguistic analysis of templates is lacking.

Methods

We reviewed the websites of 124 REBs for their templates. These included English language medical school REBs in Australia/New Zealand (n = 23), Canada (n = 14), South Africa (n = 8), the United Kingdom (n = 34), and a geographically-stratified sample from the United States (n = 45). Template language was analyzed using Coh-Metrix linguistic software (v.3.0, Memphis, USA). We evaluated the proportion of REBs with five key linguistic outcomes at or below grade eight. Additionally, we compared quantitative readability to the REBs' own readability standards. To determine if the template's country of origin or the presence of a local REB readability standard influenced the linguistic variables, we used a MANOVA model.

Results

Of the REBs who provided templates, 0/94 (0%, 95% CI = 0-3.9%) provided templates with all linguistic variables at or below the grade eight level. Relaxing the standard to a grade 12 level did not increase this proportion. Further, only 2/22 (9.1%, 95% CI = 2.5-27.8) REBs met their own readability standard. The country of origin (DF = 20, 177.5, F = 1.97, p = 0.01), but not the presence of an REB-specific standard (DF = 5, 84, F = 0.73, p = 0.60), influenced the linguistic variables.

Conclusions

Inappropriate language in templates is an international problem. Templates use words that are long, abstract, and unfamiliar. This could undermine the validity of participant informed consent. REBs should set a policy of screening templates with linguistic software.

SUBMITTER: Villafranca A

PROVIDER: S-EPMC5287453 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

The Appropriateness of Language Found in Research Consent Form Templates: A Computational Linguistic Analysis.

Villafranca Alexander A Kereliuk Stephanie S Hamlin Colin C Johnson Andrea A Jacobsohn Eric E

PloS one 20170201 2

<h4>Background</h4>To facilitate informed consent, consent forms should use language below the grade eight level. Research Ethics Boards (REBs) provide consent form templates to facilitate this goal. Templates with inappropriate language could promote consent forms that participants find difficult to understand. However, a linguistic analysis of templates is lacking.<h4>Methods</h4>We reviewed the websites of 124 REBs for their templates. These included English language medical school REBs in Au ...[more]

PMID: 28146568

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Project description:ImportanceEmotional exhaustion (EE) rates in healthcare workers (HCWs) have reached alarming levels and been linked to worse quality of care. Prior research has shown linguistic characteristics of writing samples can predict mental health disorders. Understanding whether linguistic characteristics are associated with EE could help identify and predict EE.ObjectivesTo examine whether linguistic characteristics of HCW writing associate with prior, current, and future EE.Design setting and participantsA large hospital system in the Mid-West had 11,336 HCWs complete annual quality improvement surveys in 2019, and 10,564 HCWs in 2020. Surveys included a measure of EE, an open-ended comment box, and an anonymous identifier enabling HCW responses to be linked across years. Linguistic Inquiry and Word Count (LIWC) software assessed the frequency of one exploratory and eight a priori hypothesized linguistic categories in written comments. Analysis of covariance (ANCOVA) assessed associations between these categories and past, present, and future HCW EE adjusting for the word count of comments. Comments with <20 words were excluded.Main outcomes and measuresThe frequency of the linguistic categories (word count, first person singular, first person plural, present focus, past focus, positive emotion, negative emotion, social, power) in HCW comments were examined across EE quartiles.ResultsFor the 2019 and 2020 surveys, respondents wrote 3,529 and 3,246 comments, respectively, of which 2,101 and 1,418 comments (103,474 and 85,335 words) contained ≥20 words. Comments using more negative emotion (p < 0.001), power (i.e., references relevant to status, dominance, and social hierarchies, e.g., own, order, and allow) words (p < 0.0001), and words overall (p < 0.001) were associated with higher current and future EE. Using positive emotion words (p < 0.001) was associated with lower EE in 2019 (but not 2020). Contrary to hypotheses, using more first person singular (p < 0.001) predicted lower current and future EE. Past and present focus, first person plural, and social words did not predict EE. Current EE did not predict future language use.ConclusionFive linguistic categories predicted current and subsequent HCW EE. Notably, EE did not predict future language. These linguistic markers suggest a language of EE, offering insights into EE's etiology, consequences, measurement, and intervention. Future use of these findings could include the ability to identify and support individuals and units at high risk of EE based on their linguistic characteristics.

Dataset Information

The Appropriateness of Language Found in Research Consent Form Templates: A Computational Linguistic Analysis.

Background

Methods

Results

Conclusions

Publications

The Appropriateness of Language Found in Research Consent Form Templates: A Computational Linguistic Analysis.

Similar Datasets

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