FACTORS RELATED TO DECISION-MAKING WITHIN INTERPROFESSIONAL TEAMS: A SCOPING REVIEW EXTENDED TO AN ONLINE ENVIRONMENT
Bibliographic record
Abstract
This scoping review provides a comprehensive synthesis of the various factors associated with interprofessional team decision-making. This review is unique in that it includes a broad number of factors relevant to a variety of health settings and professionals involved in team decision-making. Arksey and O’Malley’s methodological framework was used to explore empirical studies following the established protocol. First, clearly developed and inclusive search criteria were specified to find studies on interprofessional team decision-making. This review located 34136 abstracts; a total of 218 met the inclusion criteria. Second, the variety of factors were classified broadly as occurring at the individual, interpersonal, and organizational levels. These factors were further grouped as individual: attitudes, gender, expertise, personality characteristics, and professional identity; interpersonal: communication, coordination, hierarchy, leadership, role definition, shared understanding, team characteristics; and organizational: evaluation and feedback, organizational structure/culture, procedures, and resources. Our next study draws on these findings to determine how decision-making occurs in an online case consultation environment. Specifically, our goal is to examine the role of expertise and hierarchy, found in our scoping review to affect decision-making. Social work and school psychology students (low expertise) will be invited to participate in online case consultations. Upon hearing an incorrect diagnosis given by students in other professional programs, including medicine (higher status hierarchy), we will observe whether they change their correct diagnosis to the same incorrect one that was stated by another team member. Clinical case consultations are a typical training activity (i.e., occur weekly) and a typical professional activity within all professional fields. Thus, it is important to determine how individual and interpersonal factors might affect clinical decision-making.
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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 | 0.038 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.035 | 0.041 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".