Exploring implicit influences on interprofessional collaboration: a scoping review
Bibliographic record
Abstract
Interprofessional collaboration (IPC) is fraught with multiple tensions. This is partly due to implicit biases within teams, which can reflect larger social, physical, organizational, and historical contexts. Such biases may influence communication, trust, and how collaboration is enacted within larger contexts. Despite the impact it has on teams, the influence of bias on IPC is relatively under-explored. Therefore, the authors conducted a scoping review on the influence of implicit biases within interprofessional teams. Using scoping review methodology, the authors searched several online databases. From 2792 articles, two reviewers independently conducted title/abstract screening, selecting 159 articles for full-text eligibility. From these, reviewers extracted, coded, and iteratively analyzed key data using a framework derived from socio-material theories. Authors found that many studies demonstrated how biases regarding dominance and expertise were internalized by team members, influencing collaboration in predominantly negative ways. Articles also described how team members dynamically adapted to such biases. Overall, there was a paucity of research that described material influences, often focusing on a single material element instead of the dynamic ways that humans and materials are known to interact and influence each other. In conclusion, implicit biases are relatively under-explored within IPC. The lack of research on material influences and the relationship among racial, age-related, and gender biases are critical gaps in the literature. Future research should consider the longitudinal and reciprocal nature of both positive and negative influences of bias on collaboration in diverse settings.
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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 | 0.049 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".