Establishing a Community of Practice for Doctoral Studies Amidst the COVID-19 Pandemic
Bibliographic record
Abstract
In this discussion paper, we describe our experience completing the first year of the doctorate in nursing program at a large urban academic centre during the COVID-19 pandemic. We highlight the current nursing shortage and the importance of supporting all nursing students, including nurses in doctoral programs, towards successful graduation. We describe the development of a virtual community of practice incorporating five key strategies: building community, fostering collaboration, strengthening connection, enhancing creativity, and promoting consistency. We believe that utilizing these strategies will contribute to our success and may be relevant to nursing leaders seeking to support the development of more doctorally prepared nurses. Participation in a community of practice early on in doctoral education will not only better prepare students for success in their program, but also continued success as they progress through their careers. It is important for students to not only make connections with peers in their area of academic study, but to also reach out to peers in other disciplines to improve both individual and interdisciplinary growth. Program administrators and educators can encourage the formation of community of practice among novice doctoral students. This encouragement can be achieved using a virtual platform, or in-person networking opportunities. Inviting incoming graduate students to connect with each other and with students from previous cohorts also fosters community of practice formation.
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.010 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".