Are We All on the Same Page? A Discourse Analysis of Interprofessional Collaboration
Bibliographic record
Abstract
PURPOSE: Interprofessional collaboration (IPC) has become a dominant idea in both medical education and clinical care as reflected in its incorporation into competency-based educational frameworks and hospital accreditation models. This study examined the published literature to explore whether a shared IPC discourse underpins these current efforts. METHOD: Using a critical discourse analysis methodology informed by Michel Foucault's approach, the authors analyzed an archive of 188 texts published from 1960 through 2011. The authors identified the texts through a search of PubMed and CINAHL. RESULTS: The authors identified two major discourses in IPC: utilitarian and emancipatory. The utilitarian discourse is characterized by a positivist, experimental approach to the question of whether IPC is useful in patient care and, if so, what features best promote successful outcomes. This discourse uses the language of "evidence" and "validity." The emancipatory discourse is characterized by a constructivist approach concerned primarily with equalizing power relations among health practitioners; its language includes "power" and "dominance." CONCLUSIONS: This study suggests that IPC is not a single, coherent idea in medical education and health care. At least two different IPC discourses exist, each with its own distinctive truths, objects, and language. The extent to which educators and health care practitioners may tacitly align with one discourse or the other may explain the tensions that have accompanied the conceptualization, implementation, and assessment of IPC. Explicit acknowledgment of and attention to these discourses could improve the coherence and impact of IPC efforts in educational and clinical settings.
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.
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 | 0.045 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".