When Jurisdictional Boundaries Become Barriers to Good Patient Care
Bibliographic record
Abstract
Canada is a pioneer in remote cancer care delivery to underserved populations; however, it is trailing behind on policies that would support clinicians in providing care using distance technologies. The current policy framework is disjointed, and discussions by professional boards about online jurisprudence associated with licensure appear to be regressive. We hope that by addressing the discrepancies in interjurisdictional practice and focusing on the key issue of "where therapy resides," we will be able to nudge dialogue and thinking closer toward the reasoning and recommendations of national telehealth organizations. We present this discussion of jurisdictional issues and e-health practice in the context of a pan-Canadian online support program developed for cancer patients and family members. Although the present paper uses online support groups as a springboard to advocate for e-health, it ultimately addresses a broader audience: that of all Canadian health care stakeholders.
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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 | 0.037 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.036 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".