Trust and Conflict in Multicultural Teams: Exploring the Unique Experiences of Immigrant Workers
Bibliographic record
Abstract
Multicultural teams (MCTs) are essential in organizations, offering diverse perspectives that enhance creativity and problem-solving while also introduce challenges, particularly in trust-building and navigating conflict. Despite the prevalence of MCTs, existing research on the factors contributing to their success remains limited. Studies have largely focused on team- or organizational-level interventions, overlooking the interpersonal and cultural nuances that shape team dynamics. While cultural diversity exacerbates conflict and shatters trust, the mechanisms through which this occurs remain underexplored. Immigrant workers’ voices are underrepresented in the literature, leaving a gap in understanding how their unique experiences shape and are shaped by trust and conflict within teams. LTC is a high-pressure environment reliant on MCTs, where emotional labor and teamwork are essential for success. Immigrant workers in LTC face additional barriers such as cultural adjustment and miscommunication which complicate trust-building and conflict resolution. This study explores how immigrant workers in LTC perceive and navigate trust and conflict, emphasizing the individual and organizational factors influencing these processes. Using a qualitative approach, six semi-structured interviews with immigrant LTC workers revealed the role of cultural differences, misaligned communication styles, and leadership in shaping trust and conflict. Participants identified stereotypes as barriers to trust, while supportive leadership and inclusive practices were fostering collaboration. By examining trust and conflict at the individual level, this research advances the literature on MCT management, offering actionable strategies to foster collaboration and inclusivity in diverse environments. These insights are particularly valuable for LTC, where effective teamwork impacts both patient care and staff well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".