The synergistic effect of collaborative interprofessional research in health care
Bibliographic record
Abstract
Background/objective: Interprofessional research collaboration is receiving increasing attention in the healthcare disciplines. The faculties creating interprofessional educational experiences for students are discovering that they have educational, clinical, and research experiences in common and are seeking opportunities to conduct collaborative research in mutual areas of interest. In this paper, issues of interprofessional research collaboration are discussed, as are barriers and strategies to minimize those barriers.Methods: The authors present research cases that reflect interprofessional collaboration. The examples that are discussed are (a) a research project entitled “UNITED in Faith, Health, and Strength: Pioneering Faith-centered, Community-based Advance Care Planning with African American Churches” conducted by faculty in nursing, public health, medicine and a doctoral student at Johns Hopkins University; and (b) a research project entitled “Relation of Olfaction and Cognition Measures to Screening for MCI” conducted by faculty and students representing nursing, pharmacy, and occupational therapy at Shenandoah University.Results/conclusion: Collaborative research proved to be valuable in addressing healthcare practice issues of concern to faculty in multiple disciplines and provided opportunities for synergistic scholarship across disciplines.
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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 | 0.117 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.003 | 0.048 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".