From parallel practice to integrative health care: a conceptual framework
Bibliographic record
Abstract
BACKGROUND: "Integrative health care" has become a common term to describe teams of health care providers working together to provide patient care. However this term has not been well-defined and likely means many different things to different people. The purpose of this paper is to develop a conceptual framework for describing, comparing and evaluating different forms of team-oriented health care practices that have evolved in Western health care systems. DISCUSSION: Seven different models of team-oriented health care practice are illustrated in this paper: parallel, consultative, collaborative, coordinated, multidisciplinary, interdisciplinary and integrative. Each of these models occupies a position along the proposed continuum from the non-integrative to fully integrative approach they take to patient care. The framework is developed around four key components of integrative health care practice: philosophy/values; structure, process and outcomes. SUMMARY: This framework can be used by patients and health care practitioners to determine what styles of practice meet their needs and by policy makers, healthcare managers and researchers to document the evolution of team practices over time. This framework may also facilitate exploration of the relationship between different practice models and health outcomes.
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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".