Dataset Information

[An evaluation of the quality of health web pages using a validated questionnaire].

ABSTRACT: OBJECTIVE:The objective of the present study was to evaluate the quality of general health information in Spanish language web pages, and the official Regional Services web pages from the different Autonomous Regions. DESIGN:It is a cross-sectional study. We have used a previously validated questionnaire to study the present state of the health information on Internet for a lay-user point of view. PARTICIPANTS AND INTERVENTIONS:By mean of PageRank (Google®), we obtained a group of webs, including a total of 65 health web pages. We applied some exclusion criteria, and finally obtained a total of 36 webs. We also analyzed the official web pages from the different Health Services in Spain (19 webs), making a total of 54 health web pages. RESULTS:In the light of our data, we observed that, the quality of the general information health web pages was generally rather low, especially regarding the information quality. Not one page reached the maximum score (19 points). The mean score of the web pages was of 9.8±2.8. CONCLUSIONS:In conclusion, to avoid the problems arising from the lack of quality, health professionals should design advertising campaigns and other media to teach the lay-user how to evaluate the information quality.

SUBMITTER: Conesa Fuentes Mdel C

PROVIDER: S-EPMC7024504 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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[An evaluation of the quality of health web pages using a validated questionnaire].

Conesa Fuentes Maria del Carmen Mdel C Aguinaga Ontoso Enrique E Hernández Morante Juan José JJ

Atencion primaria 20100320 1

<h4>Objective</h4>The objective of the present study was to evaluate the quality of general health information in Spanish language web pages, and the official Regional Services web pages from the different Autonomous Regions.<h4>Design</h4>It is a cross-sectional study. We have used a previously validated questionnaire to study the present state of the health information on Internet for a lay-user point of view.<h4>Participants and interventions</h4>By mean of PageRank (Google®), we obtained a g ...[more]

PMID: 20304528

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Project description:BACKGROUND:Quality of life (QoL) is considered a key treatment outcome in bipolar disorder (BD) across research, clinical, and self-management contexts. Web-based assessment of patient-reported outcomes offer numerous pragmatic benefits but require validation to ensure measurement equivalency. A web-based version of the Quality of Life in Bipolar Disorder (QoL.BD) questionnaire was developed (QoL Tool). OBJECTIVE:This study aimed to evaluate the psychometric properties of a web-based QoL self-report questionnaire for BD (QoL Tool). Key aims were to (1) characterize the QoL of the sample using the QoL Tool, (2) evaluate the internal consistency of the web-based measure, and (3) determine whether the factor structure of the original version of the QoL.BD instrument was replicated in the web-based instrument. METHODS:Community-based participatory research methods were used to inform the development of a web-based adaptation of the QoL.BD instrument. Individuals with BD who registered for an account with the QoL Tool were able to opt in to sharing their data for research purposes. The distribution of scores and internal consistency estimates, as indicated by Cronbach alpha, were inspected. An exploratory factor analysis using maximum likelihood and oblique rotation was conducted. Inspection of the scree plot, eigenvalues, and minimum average partial correlation were used to determine the optimal factor structure to extract. RESULTS:A total of 498 people with BD (349/498, 70.1% female; mean age 39.64, SD 12.54 years; 181/498, 36.3% BD type I; 195/498, 39.2% BD type II) consented to sharing their QoL Tool data for the present study. Mean scores across the 14 QoL Tool domains were, in general, significantly lower than that of the original QoL.BD validation sample. Reliability estimates for QoL Tool domains were comparable with that observed for the QoL.BD instrument (Cronbach alpha=.70-.93). Exploratory factor analysis supported the extraction of an 11-factor model, with item loadings consistent with the factor structure suggested by the original study. Findings for the sleep and physical domains differed from the original study, with this analysis suggesting one shared latent construct. CONCLUSIONS:The psychometric properties of the web-based QoL Tool are largely concordant with the original pen-and-paper QoL.BD, although some minor differences in the structure of the sleep and physical domains were observed. Despite this small variation from the factor structure identified in the QoL.BD instrument, the latent factor structure of the QoL Tool largely reproduced the original findings and theoretical structure of QoL areas relevant to people with BD. These findings underscore the research and clinical utility of this instrument, but further comparison of the psychometric properties of the QoL Tool relative to the QoL.BD instrument is warranted. Future adaptations of the QoL Tool, including the production of an app-based version of the QoL Tool, are also discussed.

Project description:BackgroundAs the Internet becomes the number one destination for obtaining health-related information, there is an increasing need to identify health Web pages that convey an accurate and current view of medical knowledge. In response, the research community has created multicriteria instruments for reliably assessing online medical information quality. One such instrument is DISCERN, which measures health Web page quality by assessing an array of features. In order to scale up use of the instrument, there is interest in automating the quality evaluation process by building machine learning (ML)-based DISCERN Web page classifiers.ObjectiveThe paper addresses 2 key issues that are essential before constructing automated DISCERN classifiers: (1) generation of a robust DISCERN training corpus useful for training classification algorithms, and (2) assessment of the usefulness of the current DISCERN scoring schema as a metric for evaluating the performance of these algorithms.MethodsUsing DISCERN, 272 Web pages discussing treatment options in breast cancer, arthritis, and depression were evaluated and rated by trained coders. First, different consensus models were compared to obtain a robust aggregated rating among the coders, suitable for a DISCERN ML training corpus. Second, a new DISCERN scoring criterion was proposed (features-based score) as an ML performance metric that is more reflective of the score distribution across different DISCERN quality criteria.ResultsFirst, we found that a probabilistic consensus model applied to the DISCERN instrument was robust against noise (random ratings) and superior to other approaches for building a training corpus. Second, we found that the established DISCERN scoring schema (overall score) is ill-suited to measure ML performance for automated classifiers.ConclusionUse of a probabilistic consensus model is advantageous for building a training corpus for the DISCERN instrument, and use of a features-based score is an appropriate ML metric for automated DISCERN classifiers.AvailabilityThe code for the probabilistic consensus model is available at https://bitbucket.org/A_2/em_dawid/ .

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Dataset Information

[An evaluation of the quality of health web pages using a validated questionnaire].

Publications

[An evaluation of the quality of health web pages using a validated questionnaire].

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