A Model for Implementing Integrative Practice in Health Care Agencies
Bibliographic record
Abstract
Over the last few years, there has been increased awareness and use of complementary/alternative therapies (CAM) in many countries without the health care infrastructure to support it. The National Centre for Complementary and Alternative Medicine referred to the combining of mainstream medical therapies and CAM as integrative medicine. The creation of integrative health care teams will definitely result in redefining roles, but more importantly in a change in how services are delivered. The purpose of this paper is to describe a model of the necessary health care agency resources to support an integrative practice model. A logic model is used to depict the findings of a review of current evidence. Logic models are designed to show relationships between the goals of a program or initiative, the resources to achieve desired outputs and the activities that lead to outcomes. The four major resource categories necessary for implementing integrative care are within the domains of a) professional and research development, b) health human resource planning, c) regulation and legislation and d) practice and management in clinical areas. It was concluded that the system outcomes from activities within these resource categories should lead to freedom of choice in health care; a culturally sensitive health care system and a broader spectrum of services for achieving public health goals.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".