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Exploring the feasibility of Conjoint Analysis as a tool for prioritizing innovations for implementation

Overview of attention for article published in Implementation Science, May 2013
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Title
Exploring the feasibility of Conjoint Analysis as a tool for prioritizing innovations for implementation
Published in
Implementation Science, May 2013
DOI 10.1186/1748-5908-8-56
Pubmed ID
Authors

Katherine Farley, Carl Thompson, Andria Hanbury, Duncan Chambers

Abstract

In an era of scarce and competing priorities for implementation, choosing what to implement is a key decision point for many behavioural change projects. The values and attitudes of the professionals and managers involved inevitably impact the priority attached to decision options. Reliably capturing such values is challenging. This paper presents an approach for capturing and incorporating professional values into the prioritization of healthcare innovations being considered for adoption. Conjoint Analysis (CA) was used in a single UK Primary Care Trust to measure the priorities of healthcare professionals working with women with postnatal depression. Rating-based CA data was gathered using a questionnaire and then mapped onto 12 interventions being considered as a means of improving the management of postnatal depression. The 'impact on patient care' and the 'quality of supporting evidence' associated with the potential innovations were the most influential in shaping priorities. Professionals were least influenced by whether an innovation was an existing national or local priority, or whether current practice in the Trust was meeting minimum standards. Ranking the 12 innovations by the preferences of potential adopters revealed 'guided self help' was the top priority for implementation and 'screening questions for post natal depression' the least. When other factors were considered (such as the presence of routine data or planned implementation activity elsewhere in the Trust), the project team chose to combine the eight related treatments and implement these as a single innovation referred to as 'psychological therapies'. Using Conjoint Analysis to prioritise potential innovation implementation options is a feasible means of capturing the utility of stakeholders and thus increasing the chances of an innovation being adopted. There are some practical barriers to overcome such as increasing response rates to conjoint surveys before routine and unevaluated use of this technique should be considered.

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Mendeley readers

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The data shown below were compiled from readership statistics for 186 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
Canada 2 1%
Argentina 1 <1%
Switzerland 1 <1%
Unknown 180 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 17%
Student > Master 27 15%
Student > Ph. D. Student 26 14%
Other 18 10%
Student > Doctoral Student 13 7%
Other 37 20%
Unknown 34 18%
Readers by discipline Count As %
Medicine and Dentistry 30 16%
Psychology 26 14%
Nursing and Health Professions 17 9%
Social Sciences 13 7%
Business, Management and Accounting 10 5%
Other 41 22%
Unknown 49 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 10 June 2013.
All research outputs
#14,332,627
of 24,081,774 outputs
Outputs from Implementation Science
#1,438
of 1,751 outputs
Outputs of similar age
#105,781
of 198,227 outputs
Outputs of similar age from Implementation Science
#33
of 39 outputs
Altmetric has tracked 24,081,774 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,751 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
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 198,227 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.