Exploring the Effect of mHealth Technologies on Communication and Information Sharing in a Pediatric Critical Care Unit
Bibliographic record
Abstract
Communication and information sharing is an important aspect of healthcare information technology and mHealth management. A main requirement in the quality of patient care is the ability of all health care participants to communicate. Research illustrates that the complexity of communicating within the health care system hinders the quality of health care service delivery. Health informatics have been touted as a way to improve communication deficiencies, which has led to the exponential growth of health informatics integration. However, research still lags in understanding how health informatics affects patient care, health professional work routines, and the overall health care system. This study investigates the extent to which mHealth technologies influence communication information sharing patterns between interdisciplinary health care providers in the delivery of health care services. This study was conducted at Hamilton Health Sciences and through a sociotechnical approach, focuses on both the end user’s experiences with mHealth in daily work communication scenarios, and the extent to which mHealth use affects interdisciplinary communication. Results indicate that there are several mitigating factors which influence communication patterns using mHealth technologies, including: information sharing, mobility, ergonomic and system design.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".