Informational role self-efficacy: a validation in interprofessional collaboration contexts involving healthcare service and project teams
Bibliographic record
Abstract
BACKGROUND: Healthcare professionals perform knowledge-intensive work in very specialized disciplines. Across the professional divide, collaboration becomes increasingly difficult. For effective teamwork and collaboration to occur, it is considered necessary for individuals to believe in their ability to draw on their expertise and provide what others need to perform their job well. To date, however, no instruments exist to measure such a construct. METHODS: A two-study design is used to test the psychometric properties, factor structure and incremental validity of a five-item questionnaire measuring informational role self-efficacy. RESULTS: Based on parallel analysis and exploratory factor analysis, Study 1 shows a robust and reliable one-dimensional construct. Study 2 cross-validates this factor structure using confirmatory factor analysis. Study 2 also shows that informational role self-efficacy predicts proactive teamwork behaviors over and above goal similarity, interdependence, coordination and intra-team trust. CONCLUSIONS: The instrument can be used in research to assess an individual's capability beliefs in communicating his/her informational characteristics that are pertinent to the task performance of others. The construct is also shown to have value in team-building exercises.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".