Impact of Trust and Technology on Interprofessional Collaboration in Healthcare Settings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 it