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Advice-seeking during implementation: a network study of clinicians participating in a learning collaborative

Overview of attention for article published in Implementation Science, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Title
Advice-seeking during implementation: a network study of clinicians participating in a learning collaborative
Published in
Implementation Science, July 2018
DOI 10.1186/s13012-018-0797-7
Pubmed ID
Authors

Alicia C. Bunger, Nathan Doogan, Rochelle F. Hanson, Sarah A. Birken

Abstract

Successful implementation depends on the transfer of knowledge and expertise among clinicians, which can occur when professionals seek advice from one another. This study examines advice-seeking patterns among mental health clinicians participating in learning collaboratives (a multi-component implementation and quality improvement strategy) to implement trauma-focused cognitive behavioral therapy (TF-CBT). We apply transactive memory system theory, which explains how professionals access and retrieve knowledge, to examine factors associated with the evolution of advice-seeking relationships during implementation. Our aim is to unpack learning collaboratives' mechanisms by investigating how and why advice-seeking networks change, which may help us understand how implementation strategies can best target networks. Using social network analysis and a pretest-post-test design, we examined patterns in general and treatment-specific advice-seeking among 146 participants (including five clinical experts) from 27 agencies participating in a regional scale-up of TF-CBT. Surveys were administered in-person at the first and last of three in-person learning sessions (10 months apart) that comprise a core component of learning collaboratives. Participants nominated up to five individuals from whom they seek general and treatment-specific advice. Exponential random graph models (ERGMs) tested the likelihood of maintaining or forming advice-seeking relationships based on indicators of expertise quality, accessibility, need, and prior advice-seeking relationships. Participants formed or maintained advice-seeking relationships with those who possess perceived expertise (e.g., learning collaborative faculty experts, supervisors, and those with greater field experience than themselves). Participants also tended to seek advice from those within the same organization and with similar disciplinary training, highlighting the importance of expertise accessibility. Prior relationships and network structural features were associated with advice-seeking, indicating that participants built on existing social ties. Advice-seeking did not vary based on participants' role or experience. Given the importance of accessible clinical expertise and ongoing supervision for delivering treatment with fidelity, learning collaboratives may support implementation by promoting clinicians' awareness of and access to others' expertise, especially those with substantial expertise to share (e.g., faculty experts and supervisors). Future controlled studies are needed to verify the effectiveness of learning collaboratives for building networks that connect clinicians and experts and for improving implementation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 28 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 105 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 14%
Student > Master 14 13%
Student > Doctoral Student 11 10%
Researcher 9 9%
Student > Bachelor 5 5%
Other 17 16%
Unknown 34 32%
Readers by discipline Count As %
Social Sciences 16 15%
Medicine and Dentistry 14 13%
Psychology 14 13%
Nursing and Health Professions 9 9%
Computer Science 3 3%
Other 12 11%
Unknown 37 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 August 2018.
All research outputs
#2,205,645
of 25,765,370 outputs
Outputs from Implementation Science
#426
of 1,821 outputs
Outputs of similar age
#43,353
of 342,059 outputs
Outputs of similar age from Implementation Science
#10
of 40 outputs
Altmetric has tracked 25,765,370 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,821 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. This one has done well, scoring higher than 76% 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 342,059 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 87% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.