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Deriving population norms for the AQoL-6D and AQoL-8D multi-attribute utility instruments from web-based data

Overview of attention for article published in Quality of Life Research, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

policy
1 policy source
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1 X user
facebook
1 Facebook page
wikipedia
4 Wikipedia pages

Citations

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93 Dimensions

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51 Mendeley
Title
Deriving population norms for the AQoL-6D and AQoL-8D multi-attribute utility instruments from web-based data
Published in
Quality of Life Research, June 2016
DOI 10.1007/s11136-016-1337-z
Pubmed ID
Authors

Aimee Maxwell, Mehmet Özmen, Angelo Iezzi, Jeff Richardson

Abstract

(i) to demonstrate a method which ameliorates the problem of self-selection in the estimation of population norms from web-based data and (ii) to use the method to calculate population norms for two multi-attribute utility (MAU) instruments, the AQoL-6D and AQoL-8D, and population norms for the sub-scales from which they are constructed. A web-based survey administered the AQoL-8D MAU instrument (which subsumes the AQoL-6D questionnaire), to members of the public along with the AQoL-4D which has extant population norms. Age, gender and the AQoL-4D were used as post-stratification auxiliary variables to construct weights to ameliorate the potential effects of self-selection associated with web-based surveys. The weights were used to estimate unbiased population norms. Standard errors from the weighted samples were calculated using Jackknife estimation. For both AQoL-6D and AQoL-8D, physical health dimensions decline significantly with age. In contrast, for the majority of the psycho-social dimensions there is a significant U-shaped profile. The net effect is a shallow U-shaped relationship between age and both the AQoL-6D and AQoL-8D utilities. This contrasts with the almost monotonic decline in the utilities derived from the AQoL-4D and SF-6D MAU instruments. Post-stratification weights were used to ameliorate potential bias in the derivation of norms from web-based data for the AQoL-6D and AQoL-8D. The methods may be used generally to obtain norms when suitable auxiliary variables are available. The inclusion of an enlarged psycho-social component in the two instruments significantly alters the demographic profile.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 50 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 22%
Researcher 10 20%
Student > Master 9 18%
Lecturer 2 4%
Student > Bachelor 2 4%
Other 7 14%
Unknown 10 20%
Readers by discipline Count As %
Psychology 9 18%
Medicine and Dentistry 8 16%
Nursing and Health Professions 6 12%
Economics, Econometrics and Finance 2 4%
Social Sciences 2 4%
Other 9 18%
Unknown 15 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 02 June 2021.
All research outputs
#4,548,630
of 22,940,083 outputs
Outputs from Quality of Life Research
#417
of 2,906 outputs
Outputs of similar age
#80,863
of 352,522 outputs
Outputs of similar age from Quality of Life Research
#8
of 71 outputs
Altmetric has tracked 22,940,083 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,906 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 85% 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 352,522 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 71 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.