Branded Care: The Policy Implications of Pharmaceutical Industry-Funded Nursing Care Related to Specialty Medicines
Bibliographic record
Abstract
An increasing proportion of new drugs approved for market worldwide are now high cost, specialty medicines. Pharmaceutical marketers face the challenge of convincing payers, prescribers, and patients that the cost and complexity of care associated with specialty medicines is worth the trouble, and now offer patient support programs, free of charge, to patients prescribed their drug. We conducted a secondary, qualitative, interpretive analysis of 24 interviews with leaders of patient groups and members of hospital formulary committees in Australia to describe the work of pharmaceutical company-employed or contracted nurses who provide support to patients prescribed specialty medicines, and to prompt discussion around the policy implications of relying on industry-funded nursing care within publicly funded health systems. Participants affirmed the value of specialist, holistic, person-centered nursing care, but perceived gaps within the public health system related to the availability and provision of nursing care for people living with chronic disease. Consequently, participants described the pharmaceutical industry as addressing health system gaps through sponsorship or direct provision of medication-related nursing care, but recognized that care was contingent on commercial interest. Participants highlighted a number of ethical and policy concerns stemming from industry-funded nursing care of people prescribed specialty medicines related to patient safety, continuity of care, inducement to prescribe, and health equity. This analysis suggests that outsourcing necessary medication-related care to pharmaceutical companies has implications for the health system and equitable, sustainable pharmaceutical policy that extend far beyond the care encounter.
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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 | 0.031 | 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 | 0.015 | 0.034 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| 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".