Innovating clinical nursing education using virtual technology to combat the effects of COVID-19: A qualitative study
Bibliographic record
Abstract
BACKGROUND: At the beginning of the COVID-19 pandemic, social distancing effects halted in-person clinical placements in nursing programs at all Canadian universities. To mitigate educational disruption, clinical practicums were pivoted to online delivery, despite knowledge gaps on the perspectives of nursing students and community liaisons on using virtual technology to facilitate clinical placements. OBJECTIVE: Accordingly, we explored the impact of using innovative virtual technology to combat the social distancing effects of the COVID-19 pandemic on students' and community liaisons' experiences in a clinical community health nursing course. METHODS: Using a descriptive qualitative research approach, we evaluated an innovative online clinical placement approach which we implemented to combat the disruptions of social distancing guidelines. Forty-five nursing students were grouped and paired with five community health organizations to create community-led health promotion projects using online videoconferencing. Upon completion of their practicum, six nursing students and four community liaisons participated in virtual individual interviews. Data was analyzed using a thematic analysis approach. RESULTS: Three themes were developed: 1) Openness to change, 2) Effective virtual communication, and 3) Creation of learning spaces that foster nurturing relationships. On the theme of 'Openness to Change,' participants voiced the need to shift focus from common learning approaches to new, untested options. On the theme of 'Effective virtual communication,' participants valued the critical role of thorough virtual communication in online learning. On the theme of 'Creation of learning spaces that foster nurturing relationships,' participants appreciated the role of relationships, careful planning, and organization of virtual learning spaces for program success. CONCLUSION: The success and viability of virtual technology in clinical education are strongly related to individual and systems approach and adaptations to support students' access to learning opportunities. Our findings could be used to enhance access to virtual clinical education for students from disadvantaged and vulnerable populations.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".