Perspective integration capability: A valid and reliable measurement instrument for assessing knowledge integration readiness in interdisciplinary collaborations
Bibliographic record
Abstract
Background: Work in science, medicine, and engineering increasingly relies on collaborations among diverse experts to solve complex problems. Despite the importance of interprofessional training and practice to enhance collaboration and knowledge integration, there is a lack of a conceptually meaningful, valid, and reliable measure of individual capacity for interdisciplinary knowledge integration. This study contributes a conceptual framework and empirical tool to facilitate both research and practice of interdisciplinary collaborations. Methods: We conduct a three-phase, five-study investigation to develop and validate a measure of individual perspective integration capability (PIC), which assesses individual willingness and ability to integrate knowledge with others during collaborative work. Phase 1 includes item generation and reduction in three studies using different samples of respondents. Phase 2 demonstrates convergent and discriminant validity with conceptually related and unrelated constructs, using a separate sample of respondents. Phase 3 tests criterion-related validity and mediation by examining the multilevel relationships between PIC and key antecedents and outcomes, using data from a unique sample of research scientists in interdisciplinary medical research teams. Results: Across the three phases of our study, the results demonstrate support for the PIC instrument's factor structure, reliability, and validity. We also demonstrated that the PIC construct has important implications for individuals engaged in interdisciplinary collaborations. Conclusions: Having a conceptually meaningful, valid, reliable, and easily administered survey instrument will facilitate further study of interdisciplinary collaboration, and the development and evaluation of integration efforts of teams engaged in convergent and translational initiatives.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".