“I never thought of it as payment”: Qualitative evaluation of workshops with advanced practice registered nurses on pharmaceutical industry payment reporting
Bibliographic record
Abstract
BACKGROUND: With the expansion of professional autonomy and prescriptive authority of advanced practice registered nurses (APRNs), interactions with industry are under greater scrutiny. As of July 1, 2021, pharmaceutical and medical device companies must publicly report all payments to APRNs through the Centers for Medicare and Medicaid Services' Open Payments website. PURPOSE: To gauge APRNs' familiarity with, and perceptions of the Open Payments database and discuss whether and how APRNs should respond. METHODOLOGY: Virtual workshops consisting of a didactic presentation and interactive exercises with APRNs recruited through professional networks, associations, and conferences. Transcripts were analyzed using a qualitative interpretive approach, grounded in an everyday ethics theoretical framework. RESULTS: Thirty-six APRN clinicians, students, and faculty participated in nine workshops. Seeing sponsored meals reported in Open Payments as "payments" prompted participants to see familiar interactions in a new way. Participants valued the enhanced transparency as a way to identify risks of bias but were concerned that reporting might undermine public trust in APRNs. Emphasizing awareness as a precursor to action, participants desired greater preparation for ensuring independence in practice. CONCLUSIONS: The importance of tackling the ethical issues associated with industry interactions is heightened within the context of an existing climate of distrust within health care. However, many participants were concerned about the effects of transparency on public trust rather than how APRNs individually or collectively can be more trustworthy. IMPLICATIONS: Open Payments can serve as a useful tool to catalyze broader conversations about ethics, integrity in decision making, and health policy advocacy.
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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 | 0.082 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".