Exploiting the Fiduciary Relationship: The Physician as Information Intermediary in Assisted Human Reproduction
Bibliographic record
Abstract
The Assisted Human Reproduction Act [AHRA] impose new information disclosure requirements on physicians in the context of assisted reproductive technologies [ARTs]. In doing so, the AHRA exploits the trust relationship between physician and patient. The AHRA requires physicians to collect a wide range of highly sensitive information from those involved in ARTs and forces physicians to disclose this information to a government the Assisted Human Reproduction Agency of Canada [Agency], which may use the information for a number of non-therapeutic purposes. As a result, the Agency indirectly receive highly sensitive patient information- information which would be difficult, if not impossible, for the Agency to collect directly. Although it may be appropriate for a physician to disclose patient information to a third party under certain circumstances, the changes to the nature of the physician's role under the AHRA are troubling. Indeed, this relationship and the broad purposes for which patient information may be used by the Agency have caused concern among those who work with ARTs. Some have speculated that the majority of patients wouldn't agree to give information to a government agency, and indeed that these information provisions will prevent some patients from seeking AHR procedures altogether. There may be some merit to these concerns. The information requirements imposed by the Agency raise important questions about how the role of the physician vis-a-vis her patient change, and whether it challenge the trust inherent in the physician-patient relationship.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.045 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.021 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".