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Estimating preferences for a dermatology consultation using Best-Worst Scaling: Comparison of various methods of analysis

Overview of attention for article published in BMC Medical Research Methodology, November 2008
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1 X user
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3 Wikipedia pages

Citations

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114 Dimensions

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Title
Estimating preferences for a dermatology consultation using Best-Worst Scaling: Comparison of various methods of analysis
Published in
BMC Medical Research Methodology, November 2008
DOI 10.1186/1471-2288-8-76
Pubmed ID
Authors

Terry N Flynn, Jordan J Louviere, Tim J Peters, Joanna Coast

Abstract

Additional insights into patient preferences can be gained by supplementing discrete choice experiments with best-worst choice tasks. However, there are no empirical studies illustrating the relative advantages of the various methods of analysis within a random utility framework. Multinomial and weighted least squares regression models were estimated for a discrete choice experiment. The discrete choice experiment incorporated a best-worst study and was conducted in a UK NHS dermatology context. Waiting time, expertise of doctor, convenience of attending and perceived thoroughness of care were varied across 16 hypothetical appointments. Sample level preferences were estimated for all models and differences between patient subgroups were investigated using covariate-adjusted multinomial logistic regression. A high level of agreement was observed between results from the paired model (which is theoretically consistent with the 'maxdiff' choice model) and the marginal model (which is only an approximation to it). Adjusting for covariates showed that patients who felt particularly affected by their skin condition during the previous week displayed extreme preference for short/no waiting time and were less concerned about other aspects of the appointment. Higher levels of educational attainment were associated with larger differences in utility between the levels of all attributes, although the attributes per se had the same impact upon choices as those with lower levels of attainment. The study also demonstrated the high levels of agreement between summary analyses using weighted least squares and estimates from multinomial models. Robust policy-relevant information on preferences can be obtained from discrete choice experiments incorporating best-worst questions with relatively small sample sizes. The separation of the effects due to attribute impact from the position of levels on the latent utility scale is not possible using traditional discrete choice experiments. This separation is important because health policies to change the levels of attributes in health care may be very different from those aiming to change the attribute impact per se. The good approximation of summary analyses to the multinomial model is a useful finding, because weighted least squares choice totals give better insights into the choice model and promote greater familiarity with the preference data.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 2%
Italy 1 <1%
Brazil 1 <1%
Austria 1 <1%
South Africa 1 <1%
Taiwan 1 <1%
Unknown 117 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 20%
Researcher 23 19%
Student > Master 16 13%
Student > Doctoral Student 7 6%
Professor > Associate Professor 5 4%
Other 19 15%
Unknown 29 23%
Readers by discipline Count As %
Economics, Econometrics and Finance 21 17%
Medicine and Dentistry 12 10%
Business, Management and Accounting 11 9%
Social Sciences 8 6%
Engineering 7 6%
Other 29 23%
Unknown 36 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 July 2021.
All research outputs
#6,425,660
of 22,829,083 outputs
Outputs from BMC Medical Research Methodology
#969
of 2,013 outputs
Outputs of similar age
#40,760
of 166,367 outputs
Outputs of similar age from BMC Medical Research Methodology
#4
of 7 outputs
Altmetric has tracked 22,829,083 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 2,013 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 166,367 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.