Patient v. God: Determining the Standard of Care for Christian Science Practitioners in Medical Negligence Cases
Bibliographic record
Abstract
When physicians and nurses make mistakes there is a clearly delineated standard of care that they must have met. When one of these professionals falls below this standard of care, they may be opened up to a lawsuit in medical malpractice. While it is clear that these types of lawsuits are rarely heard and are not often successful, the standard is set and well known. Certain religious individuals shun traditional medicine. In the case of Christian Scientists, illness is simply a physical manifestation of sin. Faith healing is the practice of prayer aimed at reducing this sin, thereby clearing up the illness. Faith healers purport to be “medical” practitioners who provide a service. This paper will analyze the law on the civil liability of faith healers in the United States. Since there is scant evidence on the subject in Canada, it examines the civil liability and standard of care of other alternative medical practitioners in the United States and in Canada to determine how faith healers should be dealt with in Canadian law. The author submits that in Canada faith healers ought to be held to the same standards as other medical practitioners in order to best protect the interests of the public.
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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 | 0.068 | 0.184 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.018 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".