Interprofessional Collaboration as a Best Practice Across the Care Continuum
Bibliographic record
Abstract
PURPOSE: Interprofessional teams are increasingly being recognized as a best practice for enhancing cooperation among multiple disciplines in delivering person-centered care and improving outcomes. Unlike previous models, such as the multidisciplinary team in which each profession or discipline remained largely siloed, with interprofessional teams collaboration occurs across disciplines. For case managers, the interprofessional team concept aligns with the collaborative, professionally diverse nature of the field of practice. As the Commission for Case Manager Certification (CCMC) states: "The practice of case management is professional and collaborative, occurring in a variety of settings where medical care, mental health care, and social supports are delivered. Services are facilitated by diverse disciplines in conjunction with the care recipient and their support system" (2024b, CCMC Definition and Philosophy, p.1). Although interprofessional teams may be more familiar in settings such as acute care, this dynamic can be found, formally and informally, across health and human services. Professional case managers who actively participate in interprofessional teams will likely find more opportunities to optimize collaboration and collective decision-making that bring out the best of every profession and discipline. PRIMARY PRACTICE SETTINGS: Interprofessional teams can be found in multiple care settings including acute care, subacute care, community-based care, palliative/end-of-life and other settings that benefit from a person-centered approach that supports successful transitions of care and improved outcomes. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: Professional case managers are valued members of interprofessional teams, in that they are typically collaborative, promote open communication, and encourage cooperation among various disciplines. Interprofessional teams, however, may require a shift in thinking away from the former multidisciplinary model, in which case managers often acted as the hub connecting the spokes of each discipline. Within interprofessional teams, the individual is at the center, and every discipline will share leadership based on the individual's needs or the treatment protocol or other intervention needed in the moment. In this way, interprofessional teams become a model for empowering and allowing each discipline to step up and address specific aspects of treatment or other interventions.
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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 | 0.108 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.045 |
| Scholarly communication | 0.031 | 0.018 |
| Open science | 0.007 | 0.038 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".