I Hear You, but Do I Understand? The Relationship of a Shared Professional Language With Quality of Care and Job Satisfaction
Bibliographic record
Abstract
In various industries, individuals from different professions have to work together in a team to achieve their collective goal. Having gone through different educations, team members speak different professional languages, which poses a challenge to communication, and coordination in interprofessional teams. A shared language is believed to improve collaboration. In this study, we examine if a shared language in interprofessional healthcare teams is associated with better relational coordination and if both are connected to higher quality of care as well as job satisfaction of the staff. We shed light on possible mechanisms between shared language, and quality of care and job satisfaction, respectively, investigating relational coordination and psychological safety as mediators. We surveyed 197 healthcare workers (HCWs) from different professions in three rehabilitation centers in Switzerland. Multiple regression analyses showed that shared language was positively related to perceived quality of care and job satisfaction. Moreover, we found evidence for a serial mediation of these relationships by relational coordination and psychological safety. We discuss implications for healthcare and other types of interprofessional teams.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".