Welcome to the Wild, Wild <scp>N</scp>orth: Conscientious Objection Policies Governing <scp>C</scp>anada's Medical, Nursing, Pharmacy, and Dental Professions
Bibliographic record
Abstract
In Canada, as in many developed countries, healthcare conscientious objection is growing in visibility, if not in incidence. Yet the country's health professional policies on conscientious objection are in disarray. The article reports the results of a comprehensive review of policies relevant to conscientious objection for four Canadian health professions: medicine, nursing, pharmacy and dentistry. Where relevant policies exist in many Canadian provinces, there is much controversy and potential for confusion, due to policy inconsistencies and terminological vagueness. Meanwhile, in Canada's three most northerly territories with significant Aboriginal populations, whose already precarious health is influenced by funding and practitioner shortages, there are major policy gaps applicable to conscientious objection. In many parts of the country, as a result of health professionals' conscientious refusals, access to some legal health services - including but not limited to reproductive health services such as abortion - has been seriously impeded. Although policy reform on conscientious conflicts may be difficult, and may generate strenuous opposition from some professional groups, for the sake of both patients and providers, such policy change must become an urgent priority.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".