Delivering the script: The educational activities of medical device industry representatives as a knowledge management strategy
Bibliographic record
Abstract
Representatives of the medical device industry are routinely present in hospitals to provide education and support related to their products which, collectively, represent a company's knowledge management strategy. Between 2021 and 2022, I undertook an interpretive, phenomenological qualitative study at a large, urban, academic medical centre in Canada to examine industry's role in practice-based education. I conducted interviews (n = 23) and focus groups (N = 2) with 36 participants working across departments in roles spanning the point-of-care to executive leadership. Drawing on social studies of the co-construction of users and technologies, I examine the implications of involving commercial interests in the acquisition, circulation, and deployment of knowledge needed to practice in concert with medical technologies. Participants described manufacturers as the source of knowledge needed to use and maintain medical products and equipment and technology transfer occurred through in-services, product samples, and trial periods. However, the circulation of knowledge did not always happen freely: industry sought to maintain its position as a key intermediary in these technology transfers by controlling the flow of knowledge needed to practice in concert with technology. Instances of breakdown in access to knowledge meant that commercial interests became apparent to end-users as they conflicted with existing norms, processes, and clinical goals. Analysis of educational activities involving medical device industry representatives provides insight into the industry's strategic knowledge management, but also analysis of new forms of power and resistance to commercial goals that originate in practice and among proximate gatekeepers for technologies to practice settings.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".