“Asset exchange”—interactions between patient groups and pharmaceutical industry: Australian qualitative study
Bibliographic record
Abstract
OBJECTIVE: To understand and report on the nature of patient group interactions with the pharmaceutical industry from the perspective of patient group representatives by exploring the range of attitudes towards pharmaceutical industry sponsorship and how, why, and when interactions occur. DESIGN: Empirical qualitative interview study informed by ethics theory. SETTING: Australian patient groups. PARTICIPANTS: 27 participants from 23 Australian patient groups that represented diverse levels of financial engagement with the pharmaceutical industry. Groups were focused on general health consumer issues or disease specific topics, and had regional or national jurisdictions. ANALYSIS: Analytic techniques were informed by grounded theory. Interview transcripts were coded into data driven categories. Findings were organised into new conceptual categories to describe and explain the data, and were supported by quotes. RESULTS: A range of attitudes towards pharmaceutical industry sponsorship were identified that are presented as four different types of relationship between patient groups and the pharmaceutical industry. The dominant relationship type was of a successful business partnership, and participants described close working relationships with industry personnel. These participants acknowledged a potential for adverse industry influence, but expressed confidence in existing strategies for avoiding industry influence. Other participants described unsatisfactory or undeveloped relationships, and some participants (all from general health consumer groups) presented their groups' missions as incompatible with the pharmaceutical industry because of fundamentally opposing interests. Participants reported that interactions between their patient group and pharmaceutical companies were more common when companies had new drugs of potential interest to group members. Patient groups that accepted industry funding engaged in exchanges of "assets" with companies. Groups received money, information, and advice in exchange for providing companies with marketing, relationship building opportunities with key opinion leaders, coordinated lobbying with companies about drug access and subsidy, assisting companies with clinical trial recruitment, and enhancing company credibility. CONCLUSIONS: An understanding of the range of views patient groups have about pharmaceutical company sponsorship will be useful for groups that seek to identify and manage any ethical concerns about these relationships. Patient groups that receive pharmaceutical industry money should anticipate they might be asked for specific assets in return. Selective industry funding of groups where active product marketing opportunities exist might skew the patient group sector's activity towards pharmaceutical industry interests and allow industry to exert proxy influence over advocacy and subsequent health policy.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".