Factors Enabling Communication-Based Collaboration in Interprofessional Healthcare Practice
Bibliographic record
Abstract
The healthcare system has moved from autonomous practice to a cross-disciplinary interprofessional team-based approach in which communication for collaborative care is vital. Ineffective communication contributes to the team's inability to work collaboratively and significantly increases the possibilities of mistakes occurring in the delivery of patient care. So, effective communication for collaborative care becomes necessary for ensuring patient safety. This paper aims to advance our understandings of current communication-based collaborative healthcare practices. Specifically, it explores the factors enabling communication-based inter-professional practice. A qualitative study was selected for obtaining real life experiences of healthcare professionals. Twenty-five participants participated in the study, and the descriptive interview method was used to obtain qualitative data. The enabling factors were grouped into five main themes: communication, coordination, cooperation, trust, and collaboration. Quotes from the participants are presented to augment the interpretation and enhanced description of the enabling factors. Managerial implications, areas for future research, and limitations are given besides the conclusions of the study.
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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 | 0.019 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".