ArticleLearning health systems2020
How to measure the collective intelligence of primary healthcare teams?
Article in Learning health systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 37 citations in OpenAlex.
- Assessment of interprofessional obstetric and midwifery care from the midwives' perspective using the Interprofessional Collaboration Scale (ICS).Frontiers in psychology · 2023Article
- How to measure the collective intelligence of primary healthcare teams?Learning health systems · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe capacity for teams and organizations to evolve and to thrive in ever-shifting environments is attributed to their collective intelligence. Collectively, intelligent team could prevent repetition of past mistakes and can help organizations and people work more efficiently. Researchers aimed to find a framework or a tool that could help explain collective intelligence in primary healthcare organizations.
methodsThe framework was developed iteratively following a three-step process based on the Pragmatic utility concept analysis, each step fetching data from both literature and the team's expertise: (i) finding an existing framework, (ii) developing an initial framework, (iii) testing and refining the framework.
resultsA broad literature search led researchers to focus more specifically on two interrelated frameworks, both concepts were created within the educational field. We first adapted these concepts to healthcare teams, then to the increasing interdisciplinarity of primary healthcare teams. We also subdivided the framework into clinical or organizational domain. Finally, we performed a secondary analysis from existing data of a larger project that aimed to evaluate seven primary care teams in Quebec.
conclusionsThis first attempt to conceptualize collective intelligence in a way that is specific to primary healthcare teams helps identify strengths and areas in which teams could potentially improve. From a theoretical perspective, the framework facilitates understanding of the concept of collective intelligence in primary healthcare teams. Our current results show a strong potential for this tool, but other tests and systematic validations are to be expected in order to better link collective intelligence and team performance.
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Registered trials
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