How to measure the collective intelligence of primary healthcare teams?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: The 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. METHODS: The 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. RESULTS: A 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. CONCLUSIONS: This 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it