Clinician, student and faculty perspectives on the audiology-industry interface: implications for ethics education
Bibliographic record
Abstract
Objective: Supporting audiologists to work ethically with industry requires theory-building research. This study sought to answer: How do audiologists view their relationship with industry in terms of ethical implications? What do audiologists do when faced with ethical tensions? How do social and systemic structures influence these views and actions?Design: A constructivist grounded theory study was conducted using semi-structured interviews of clinicians, students and faculty.Study sample: A purposive sample of 19 Canadian and American audiologists was recruited with representation across clinical, academic, educational and industry work settings. Theoretical sampling of grey literature occurred alongside audiologist sampling. Interpretations were informed by the concepts of ethical tensions as ethical uncertainty, dilemmas and distress.Results: Findings identified the audiology–industry relationship as symbiotic but not wholly positive. A range of responses included denying ethical tensions to avoiding any industry interactions altogether. Several of our participants who had experienced ethical distress quit their jobs to resolve the distress. Systemic influences included the economy, professional autonomy and the hidden curriculum.Conclusions: In direct response to our findings, the authors suggest a move to include virtues-based practice, an explicit curriculum for learning ethical industry relations, theoretically-aligned ethics education approaches and systemic and structural change.
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".