The Scope of Protection Provided by Apology Legislation in Canada with Emphasis on the Patient-Health Care Provider Relationship
Bibliographic record
Abstract
The question of whether prior apologies should be admissible in legal proceedings as proof of fault or liability has been debated in every Canadian province and territory within the last decade. As a result all but two jurisdictions, whether through stand-alone statutes or amendments to existing legislation, have limited, at least to some degree, the extent to which apologies can be used in subsequent proceedings. There is a growing body of literature on apologies,' much of it written from a health practitioner, philosophical, ethical, or conflict resolution perspective, but less in the way of ongoing legal analysis. In terms of legal analysis, there are only a handful of academic articles, and a number of commentaries, frequently by practising lawyers in response to the passage of apology legislation in their province or territory. As well, there are several brief judicial decisions, which we will discuss below.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".