Measures of interprofessional education and collaboration
Bibliographic record
Abstract
Healthcare and social services professionals are being called to engage in interprofessional education (IPE) and interprofessional collaboration (IPC) in order to provide efficient and effective care to clients and patients. As such, it is important to conduct research that contributes to evaluation of collaborative practice. A necessary component to any strong quantitative research methodology is the type of instruments used for data collection. However, identifying valid and reliable instruments to use in this area of research can be a daunting task. The purpose of this paper is to review the quantitative measures (i.e., surveys and questionnaires) described in the interprofessional literature. Twenty-three instruments were identified and analyzed for validity and reliability statistics, sample size, ease of access to items on measure, and applicability of measure to diverse professional populations. The two primary measures reviewed are the Readiness for Interprofessional Learning Scale (Parsell & Bligh, 1998 ) and the Interdisciplinary Education Perception Scale (Luecht, Madsen, Taugher, & Petterson, 1990 ). Limited information existed for the remaining measures. Despite the number of measures available for assessing and evaluating IPE and IPC, most lack sufficient theoretical and psychometric development. Several issues that impact the development of sound measures are discussed and implications for future IPC are proposed.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".