[THE APPLICATION OF AN INTEGRATED MODEL OF PARTNERSHIP- PATIENT IN THE PROFESSIONALS OF THE HEALTH TRAINING: TOWARDS NEW ONE HUMANIST PARADIGM AND ETHICS OF CO-CONSTRUCTION KNOWLEDGES IN HEALTH].
Bibliographic record
Abstract
This article presents the "Montreal model", offering the patient, if he wishes, to become a patient partner for all the decisions that concerns him. We are presently witnessing, presently, major transformations in western societies. Chronic diseases affect a growing proportion of the population, leading a transition from the ascendancy of acute care to an ascendancy of chronic care. Other societal factors such as the consumerism, the individualism, the democratization of the medical information (accelerated by the advent of Internet) and the casualization of a growing part of citizens influence this evolution. In this context, where the nature and the level of patients and their families needs are changing as their expectations towards healthcare systems, one of the promising ways to answer these stakes is a greater participation of patients in their own care. Since 2010, a new relational model, based on the partnership of care between patients and healthcare professionals, was developed in the medicine Faculty of the University of Montreal. This model leans on the recognition of the experiential knowledge of the patients in complementarity with the scientific knowledge of the healthcare professionals. It's the continuity of the participation and the engagement of patients which can be applied to the whole healthcare system, in individual or collective relationship in interdisciplinary. This model is presently in implementation phase in Quebec in the training of the healthcare professionals, in healthcare organisations, as well as in the medical education and in research.
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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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.108 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".