Making it real: the institutionalization of collaboration through formal structure
Bibliographic record
Abstract
Collaboration has achieved widespread acceptance as an indispensable element of healthcare delivery in recent decades, despite modest evidence for its impact on healthcare outcomes. Attempts to understand this seeming paradox have been based mostly in functionalist or conflict-theoretical approaches. Currently lacking, however, is an articulation of how collaborative ideals are embedded in broadly shared beliefs about what healthcare is and how it operates. In this article, we examine how language used in the CanMEDS competency framework and in two guides for Family Health Teams construct idealized versions of rational, autonomous physicians and primary care organizations, respectively. Informed by phenomenological sociology and neo-institutional theory, we characterize these documents as elements of formal structure, the putative "blueprints" for healthcare planning and activity. Drawing on this analysis, we argue that these documents and "collaborative" formal structures in general, not only function as tools to make healthcare more collaborative, but also create an appearance of "real" collaboration, independently of the realities of practice. We argue that they thus instill confidence that the current healthcare system functions according to deep-seated societal values of justice and progress. We conclude by emphasizing the potentially distorting influence of this on efforts to understand and improve healthcare.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.092 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| 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".