Mutual understanding in multi-disciplinary primary health care teams
Bibliographic record
Abstract
Empirical research on multi-disciplinary health care teams has yet to explore the development of mutual understanding between team members in the course of their collective clinical decision-making. This paper addresses this gap in the literature directly by examining changes in mutual understanding and the extent to which its facilitation is shared by individual members of multi-disciplinary health care teams. A Habermasian theoretical framework is used to operationalize mutual understanding. Social network analysis is used to analyze survey data on team-based clinical decision-making collected from multi-disciplinary health care teams in a Canadian province. The results of the study indicate that mutual understanding between team members ebbs and flows over the course of their collective clinical decisions. Further, as the extent of mutual understanding within the team increases, its facilitation becomes more equally shared among team members. The paper closes by specifying a practical outcome of the future work: a typology of clinical decisions that health care teams are able to use as an evaluation tool to assess how effectively they are making collective clinical decisions. As an evaluation tool, the typology would foster open and deliberative discussion, enable critical self-reflection, and thereby further enhancing mutual understanding within the teams.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".