Perceptions of Live Streaming Compared With an In-Person Nursing Conference
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to understand registered nurses' (RNs') perceptions of attending a live streaming versus in-person continuing education event. BACKGROUND: During the COVID-19 global pandemic, in-person continuing education events for healthcare providers required conversion to digital platforms. Literature is sparse regarding healthcare providers' perceptions on attending a live streaming continuing education event. METHODS: Registered nurses completed a survey after a live streaming research conference from a large US healthcare system. Likert-scale survey items were analyzed using descriptive statistics and open-ended questions with content analysis and thematic coding. RESULTS: A total of 219 RNs participated. The RNs reported an overall positive experience with the live streaming event and indicated a preference for this platform for the future. Three benefits emerged: savings, self-care and safety, and user-friendly. Perceived drawbacks were coded with 3 themes: technical issues, impaired focus, and social/networking challenges. CONCLUSIONS: Despite challenges, live streaming conferences may be satisfying and preferable for nurses.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".