Bibliographic record
Abstract
Understanding and adhering to codes of conduct is important in healthcare. Collaboration is required to drive innovation and transform the healthcare system. Little is known about the industry—health system leaders/executives relationship. In Canada, the medical technology industry consults health system leaders to align business models with the health systems' needs, and understanding the implications of these fairly new relationships as related to ethics and codes of conduct. This research reflects agreement that ethical conduct and adherence to one's code of conduct or ethics is paramount. Any conflicts of interests can compromise public trust and inhibit much-needed public—private synergy. There is a high level of interest on the part of health system leaders to develop a more robust approach to reciprocally sharing and discussing codes of conduct. Opportunities are identified to improve both discussion and education. Instead of restricting industry, the focus should be on transparency and actively managing these relationships. Ethical and transparent partnerships are critical to the advancement of high-quality and cost-effective patient care. As health leaders are faced with the challenges of financial sustainability, efficiency, and quality, the medical technology industry can be instrumental in translating global best practices and supporting system innovations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.138 | 0.334 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".