Understanding decision-making in interprofessional team meetings through interpretative repertoires and discursive devices
Bibliographic record
Abstract
Health practitioners of the geriatrics ward in a teaching hospital participate in interprofessional team meetings to agree on treatment and discharge care plans for their patients suffering from chronic illnesses and co-morbidities and in need of coordinated assessments and care. We turn to the ideas in critical discursive psychology to grow a much-needed research area of examining the language-in-use and its effects in team decision-making. Specifically we explore how healthcare team members use language to perform collaboration or disengagement, creating different subject positionings for themselves and others out of a backcloth of discursive resources and practices. We observed and transcribed 108 case discussions and analyzed them for interpretative repertoires and discursive devices. During the first half of the team discussions, the members of various health professions employed the empiricist and lifeworld interpretative repertoires and the discursive strategy of perspective-taking, articulating these through formulations and questions. We use the notion of argumentative texture to better understand why an administrative structural support like protected turn-taking in team meetings is not enough to promote interprofessional collaboration. We conclude that health practitioners can improve their contributions and subject positionings at team meetings and consequently patient-care, by identifying habitually deployed linguistic resources depicting professional knowledge, and augmenting these with Other-oriented perspectives in their repertoires. By expanding their range of discursive repertoires and recognizing that discursive practices are embedded in the bigger context or argumentative texture of institutional and societal discourses, norms, values, beliefs and practices, interprofessional teams can work to improve communication and knowledge-sharing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.011 | 0.063 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".