Bibliographic record
Abstract
BACKGROUND: Dietitians of Canada has endorsed the Principles and Framework for Enhancing Interdisciplinary Collaboration in Primary Health Care (EICP) created by the EICP Initiative. The Initiative focused on the conditions required for health professionals to work together in the most effective and efficient way, so that they can produce the best health outcomes for individuals and their families--the patients, clients, and consumers of our national health system. The Initiative was spearheaded by a Steering Committee of 11 national health professional organizations, and brought together leaders, health professionals, and key stakeholders in Canada's primary health care system in a change process designed to facilitate more interdisciplinary collaboration. PRINCIPLES AND FRAMEWORK: In the context of the EICP Initiative, the Principles are values shared by stakeholders. They are critical to the establishment of collaboration and teamwork to achieve the best health outcomes. The elements of the Principles are patient/client engagement, a population health approach, the best possible care and services, access, trust and respect, and effective communication. The Framework builds upon these Principles and is composed of the structural and process elements required to support collaborative primary health care. The elements of the Framework are health human resources, funding, liability, regulation, information and communications technology, management and leadership, and planning and evaluation. CONCLUSIONS: The Boards of Directors of the ten health professions leading the EICP Initiative agreed upon the values and key structural and process elements that need to be put in place to enhance interdisciplinary collaboration in Primary Health Care in Canada. Dietitians of Canada will continue to seek opportunities to further the change process started by EICP. Approximately 40 research papers and a toolkit to help primary heath care providers work together have been produced.
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 | 0.049 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".