President & Chief Executive Officer
Bibliographic record
Abstract
Thank you for the opportunity to respond to the Guide on issues related to interprofessional collaboration among Health Colleges and Professionals. From a cancer system perspective, we know that the current models of care delivery will be inadequate to meet the projected increased incidence of new cancer cases in Ontario. The inadequacy of the current system is that professionals are too discipline-centric, focused on activities that too often are restrictive, with little ability to be flexible to meet the needs of the patient population. Often the inflexibility is deemed to relate to scope of practice, or in the case of Nurse Practitioners, lists of what they can and cannot do. Thus the Minister’s request of HPRAC to examine the process is timely and critical to instil change in the system, while protecting the public in every domain of care. The sustainability of the health care system depends on the willingness and ability of all health professionals to refocus their practice toward interprofessional models of service delivery in order to effectively engage in promoting health and well-being of people, irrespective of whether they are well or ill. This is the essence of professional practice, and the regulatory bodies and professional disciplines need to collaborate so to move beyond talking to actually making change happen. It is time for us to lead the next generation of change to effect new roles,
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.180 | 0.133 |
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".