The impetus of COVID-19 in transforming nursing education through informatics
Bibliographic record
Abstract
Background: National nursing organizations worldwide have called for the inclusion of digital tools in nursing curricula to prepare future nurses to use digital tools in their professional practice. Objective: This study explored the experiences of nursing faculty with respect to integrating digital tools in their teaching to support undergraduate student learning during the COVID-19 pandemic. Method: This study was a focused ethnography featuring semi-structured interviews, field notes, and artifacts. Data were analyzed concurrently with data collection, using thematic analysis. A total of 21 participants from nine undergraduate nursing programs in Western Canada were interviewed as part of a larger study. This paper focuses on the 12 participants who were interviewed during the COVID-19 pandemic. Results: This paper discusses four themes related to faculty experiences using digital tools to support student learning during the COVID-19 pandemic: (1) the pandemic, (2) enablers, (3) challenges, and (4) learners. Faculty quickly transitioned from in-person to remote and virtual teaching, changing how they engaged with digital tools. Faculty were responsive and collectively rose to the challenges they faced, which suggests their agility and willingness to embrace informatics. Conclusion: The pandemic created an impetus for nursing faculty to utilize more digital tools to sustain the continuity of education. Further support and resources are needed to increase faculty informatics capacity in a more systematic way.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| 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".