Impact of Trust and Technology on Interprofessional Collaboration in Healthcare Settings
Bibliographic record
Abstract
The increases in complexity of patient care, healthcare costs, and technological advancements shifted the healthcare delivery to interprofessional collaborative care. The study aims for identifying the factors influencing the quality of team collaboration. The study examines the impact of trust and technology orientation on collaboration with the mediating effects of communication, coordination and cooperation. A questionnaire survey was conducted to gather data from healthcare professionals (N=216). Statistical analysis conducted for this study include correlations, factor analysis with reliability and validity tests and Partial Least Squares (PLS) method. The results of the study validate that (i) collaboration has positive and significant relationship with coordination, and cooperation; (ii) trust has positive and significant relationship with communication, coordination, and cooperation; and (iii) technology orientation has positive and significant relationship with cooperation but not with communication and coordination. The research and managerial implications of these factors are given in discussion. As with most empirical studies, the subjectivity of the opinion of respondents present some limitations to generalization. Other limitations include the lack of availability and use of standard measures for various constructs in the research model. The results can be used by healthcare professionals and managers to advance their understanding on the impact of trust and technology on collaboration mediating communication, coordination and cooperation practices. The significant value of this study is the identification of the factors influencing the quality of team collaboration in healthcare industry.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".