The Interprofessional Socialization and Valuing Scale: A tool for evaluating the shift toward collaborative care approaches in health care settings
Bibliographic record
Abstract
BACKGROUND: There is a need for tools by which to evaluate the beliefs, behaviors, and attitudes that underlie interprofessional socialization and collaborative practice in health care settings. METHOD: This paper introduces the Interprofessional Socialization and Valuing Scale (ISVS), a 24-item self-report measure based on concepts in the interprofessional literature concerning shifts in beliefs, behaviors, and attitudes that underlie interprofessional socialization. The ISVS was designed to measure the degree to which transformative learning takes place, as evidenced by changed assumptions and worldviews, enhanced knowledge and skills concerning interprofessional collaborative teamwork, and shifts in values and identities. The scales of the ISVS were determined using principal components analysis. RESULTS: The principal components analysis revealed three scales accounting for approximately 49% of the variance in responses: (a) Self-Perceived Ability to Work with Others, (b) Value in Working with Others, and (c) Comfort in Working with Others. These empirically derived scales showed good fit with the conceptual basis of the measure. CONCLUSION: The ISVS provides insight into the abilities, values, and beliefs underlying socio-cultural aspects of collaborative and authentic interprofessional care in the workplace, and can be used to evaluate the impact of interprofessional education efforts, in house team training, and workshops.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".