Dual-Loyalty: The Wicked Problem of Corporatization in Health Professions
Bibliographic record
Abstract
. Increasingly, for-profit corporations are delivering a greater portion of healthcare services. While this may enhance operational efficiency and organizational effectiveness, it may raise questions about safeguarding of patients’ interests and supporting the autonomy and professional judgement of individual professionals who may work as employees. In such corporate healthcare settings, there may often be leaders who themselves are licensed professionals. These individuals may not personally provide care to patients but direct the work of other professionals or establish corporate policies, practices, and cultures that shape the practice of others. In such situations, the problem of “dual loyalties” may arise, in which licensed healthcare professionals must simultaneously reconcile a professional/ethical and fiduciary responsibility to act in the best interests of patients with a corporate responsibility to maximize shareholder value. While many different agencies—including governments, accreditation bodies, and industry agencies—participate in the regulation of corporatized healthcare, the specific responsibilities and opportunities for licensing bodies to ensure appropriate management of dual loyalties has not been widely discussed. The multi-faceted and highly interconnected nature of this wicked problem opens opportunities for discussion and reflection within licensing bodies regarding how best to use mechanisms such as Codes of Ethics, Standards of Practice, and complaints/investigation systems in the context of dual loyalties in corporatized practice settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".