Struggles in the diffusion of high-end medical technology in Switzerland and in Canada
Bibliographic record
Abstract
This book examines the diffusion process for a complex medical technology, the PET scanner, in two different health care systems, one of which is more market-oriented (Switzerland) and the other more centrally managed by a public agency (the province of Quebec in Canada). More specifically, this research draws on institutional and socio-political theories of the diffusion of innovations to examine how institutional contexts affect processes of diffusion. The study finds that diffusion proceeds more rapidly in Switzerland than in Quebec, but that processes in both jurisdictions are characterized by intense struggles among providers and between providers and public agencies. \n \nThis study shows that the institutional environment influences these processes by determining the patterns of material resources and authority available to actors in their struggles to strategically control the technology, and by constituting the discursive resources or institutional logics on which actors may legitimately draw in their struggles to give meaning to the technology in line with their interests and values. This book also illustrates how institutional structures and meanings manifest themselves in the context of specific decisions within an organizational field, and reveals the ways in which governance structures may be contested and realigned when they conflict with interests that are legitimized by dominant institutional logics. It is argued that this form of contestation and readjustment at the margins constitutes one mechanism by which institutional frameworks are tested, stretched and reproduced or redefined.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".