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Factors influencing implementation of a patient decision aid in a developing country: an exploratory study

Overview of attention for article published in Implementation Science, March 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
12 tweeters

Citations

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17 Dimensions

Readers on

mendeley
118 Mendeley
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Title
Factors influencing implementation of a patient decision aid in a developing country: an exploratory study
Published in
Implementation Science, March 2017
DOI 10.1186/s13012-017-0569-9
Pubmed ID
Authors

Wen Ting Tong, Yew Kong Lee, Chirk Jenn Ng, Ping Yein Lee

Abstract

Most studies on barriers and facilitators to implementation of patient decision aids (PDAs) are conducted in the west; hence, the findings may not be transferable to developing countries. This study aims to use a locally developed insulin PDA as an exemplar to explore the barriers and facilitators to implementing PDAs in Malaysia, an upper middle-income country in Asia. Qualitative methodology was adopted. Nine in-depth interviews (IDIs) and three focus group discussions (FGDs) were conducted with policymakers (n = 6), medical officers (n = 13), diabetes educators (n = 5) and a nurse, who were involved in insulin initiation management at an academic primary care clinic. The interviews were conducted with the aid of a semi-structured interview guide based on the Theoretical Domains Framework. The interviews were audio-recorded, transcribed verbatim and analyzed using a thematic approach. Five themes emerged, and they were lack of shared decision-making (SDM) culture, role boundary, lack of continuity of care, impact on consultation time and reminder network. Healthcare providers' (HCPs) paternalistic attitude, patients' passivity and patient trust in physicians rendered SDM challenging which affected the implementation of the PDA. Clear role boundaries between the doctors and nurses made collaborative implementation of the PDA challenging, as nurses may not view the use of insulin PDA to be part of their job scope. The lack of continuity of care might cause difficulties for doctors to follow up on insulin PDA use with their patient. While time was the most commonly cited barrier for PDA implementation, use of the PDA might reduce consultation time. A reminder network was suggested to address the issue of forgetfulness as well as to trigger interest in using the PDA. The suggested reminders were peer reminders (i.e. HCPs reminding one another to use the PDA) and system reminders (e.g. incorporating electronic medical record prompts, displaying posters/notices, making the insulin PDA available and visible in the consultation rooms). When implementing PDAs, it is crucial to consider the healthcare culture and system, particularly in developing countries such as Malaysia where concepts of SDM and PDAs are still novel.

Twitter Demographics

The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 118 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 17%
Student > Master 16 14%
Student > Bachelor 13 11%
Researcher 12 10%
Student > Doctoral Student 12 10%
Other 23 19%
Unknown 22 19%
Readers by discipline Count As %
Medicine and Dentistry 31 26%
Nursing and Health Professions 26 22%
Business, Management and Accounting 6 5%
Pharmacology, Toxicology and Pharmaceutical Science 6 5%
Computer Science 5 4%
Other 19 16%
Unknown 25 21%

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 23 March 2017.
All research outputs
#2,520,817
of 13,727,342 outputs
Outputs from Implementation Science
#696
of 1,397 outputs
Outputs of similar age
#69,143
of 262,426 outputs
Outputs of similar age from Implementation Science
#13
of 22 outputs
Altmetric has tracked 13,727,342 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,397 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.4. This one is in the 49th percentile – i.e., 49% 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 262,426 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 22 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.