Pharmaceutical Industry discursives and the marketization of nursing work: a case example
Bibliographic record
Abstract
Increasing pharmaceutical industry presence in health care research and practice has evoked critical social, political, economic, and ethical questions and concern among health care providers, ethicists, economists, and the general citizenry. The case example presented of the 'marketization' of nursing practice not only reveals the magnitude of the purview of the pharmaceutical industry, it demonstrates how that industry imparts effect upon the organization of nursing work, an area of health care professional practice where the ethical polemic of pharmaceutical industry involvement and influence has been largely ignored, and the profession of nursing conspicuously silent. Drawing on a Foucauldian dispositive analysis that troubled the complex apparatus responsible for the production of knowledge and action in the neurology subspecialty of multiple sclerosis (MS), the case discloses how the pharmaceutical industry has created compliance and adherence as clinical imperatives in the practice of MS nursing. The case makes explicit the conscious transformative self-action undertaken by MS nurses as a result of their subjectivation (marketization) and demonstrates how MS nurses have become pawns in pharmaceutical industry strategic games of power, truth, identity, and wealth creation by turning their clinical practice settings into heterodiscursive spaces of surveillance and persuasion. MS nurses have become instruments of the pharmaceutical industry, and their clinical practices ordered, organized, limited, constrained, and marketized as a result.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.031 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".