Dataset Information

REFERQUAL: a pilot study of a new service quality assessment instrument in the GP exercise referral scheme setting.

ABSTRACT:

Background

The development of an instrument accurately assessing service quality in the GP Exercise Referral Scheme (ERS) industry could potentially inform scheme organisers of the factors that affect adherence rates leading to the implementation of strategic interventions aimed at reducing client drop-out.

Methods

A modified version of the SERVQUAL instrument was designed for use in the ERS setting and subsequently piloted amongst 27 ERS clients.

Results

Test re-test correlations were calculated via Pearson's 'r' or Spearman's 'rho', depending on whether the variables were Normally Distributed, to show a significant (mean r = 0.957, SD = 0.02, p < 0.05; mean rho = 0.934, SD = 0.03, p < 0.05) relationship between all items within the questionnaire. In addition, satisfactory internal consistency was demonstrated via Cronbach's 'alpha'. Furthermore, clients responded favourably towards the usability, wording and applicability of the instrument's items.

Conclusion

REFERQUAL is considered to represent promise as a suitable tool for future evaluation of service quality within the ERS community. Future research should further assess the validity and reliability of this instrument through the use of a confirmatory factor analysis to scrutinize the proposed dimensional structure.

SUBMITTER: Cock D

PROVIDER: S-EPMC1482701 | biostudies-literature | 2006 May

REPOSITORIES: biostudies-literature

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Publications

REFERQUAL: a pilot study of a new service quality assessment instrument in the GP exercise referral scheme setting.

Cock Don D Adams Iain C IC Ibbetson Adrian B AB Baugh Phil P

BMC health services research 20060525

<h4>Background</h4>The development of an instrument accurately assessing service quality in the GP Exercise Referral Scheme (ERS) industry could potentially inform scheme organisers of the factors that affect adherence rates leading to the implementation of strategic interventions aimed at reducing client drop-out.<h4>Methods</h4>A modified version of the SERVQUAL instrument was designed for use in the ERS setting and subsequently piloted amongst 27 ERS clients.<h4>Results</h4>Test re-test corre ...[more]

PMID: 16725021

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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/ .

Dataset Information

REFERQUAL: a pilot study of a new service quality assessment instrument in the GP exercise referral scheme setting.

Background

Methods

Results

Conclusion

Publications

REFERQUAL: a pilot study of a new service quality assessment instrument in the GP exercise referral scheme setting.

Similar Datasets

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