Factors Affecting Interprofessional Collaboration when Communicating through the use of Information and Communication Technologies: A Literature Review
Bibliographic record
Abstract
Abstract Background Information and communication technologies (ICTs) are increasingly being used internationally as a cost-effective and efficient way to provide care for patients in rural and remote settings, often referred to as telemedicine. There have been various studies that have examined the effectiveness of telemedicine implementation on patient outcomes, and the factors that enable successful telemedicine program implementation. The purpose of this narrative literature review was to explore a different side of the issue, with the objective to examine the factors that affect interprofessional collaboration when communicating through the use of ICTs in telemedicine settings. Methods and Findings A total of 56 papers were included in this review. Using a narrative review design, analysis of the papers revealed several factors that act as facilitators and barriers to interprofessional collaboration when communicating through the use of ICTs. Facilitators included training and planning; ICT system supports; establishing good rapport and communication patterns; patient-centredness; willingness to adapt to and accept the technology; and key individuals providing leadership and administrative support. Barriers included technical issues; coordination and organizational challenges; and problematic relationships. Conclusions From the facilitators and barriers, recommendations have been compiled for stakeholders involved in telemedicine initiatives to consider on how to support interprofessional collaboration in telemedicine.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".