Frontline connect: Evaluating a virtual technology program to enhance patient and provider communication during COVID‐19
Bibliographic record
Abstract
RATIONALE: Since the beginning of the COVID-19 pandemic, many hospitals have reduced in-hospital visitation. In these situations, virtual communication tools have helped maintain interaction between parties. The Frontline Connect program was designed to address communication and patient care challenges by providing data-enabled devices to clinical staff in hospitals. OBJECTIVE: This study aimed to identify areas of improvement for the Frontline Connect program by: (a) evaluating communication needs, user experience, and program satisfaction; and (b) identifying potential barriers to device access or use. METHODS: We administered pre-implementation needs assessment, post-use, and exit surveys to healthcare staff at a pilot hospital site in Ontario. Recruitment was through email lists and site champions using convenience sampling. We descriptively analysed survey responses and compared the initial need statements to post-implementation use-cases identified by users. RESULTS: We received 139 needs assessments, 31 user experience assessments, and 47 exit survey responses. Most device use occurred in the emergency department and intensive care units and was facilitated by social workers, nurses, and physicians to connect patients, families, and care providers. Pre-implementation concerns were related to infection control, data security, and device privacy. In the exit survey, these were replaced by other concerns including Internet connectivity and time-intensiveness. Device utility and ease-of-use were rated 9.7/10 and 9.6/10 respectively in the user experience survey, though overall experience was rated 7.2/10 in the exit survey. Overall, respondents viewed the devices as useful and we agree with participants who suggested increased program promotion and training would likely improve adoption. CONCLUSIONS: We found that our virtual technology program for facilitating communication was positively perceived. Survey feedback indicates that a rapid rollout in response to urgent pandemic-related needs was feasible, though program logistics could be improved. The current work supports the need to improve, standardize, and sustain virtual communication programs in hospitals.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".