A community of practice for graduate students in health sciences
Bibliographic record
Abstract
Purpose The aim of this study was to describe the experiences of graduate students who participated in the community of practice (CoP) and identify areas for improvement to support academic success. Design/methodology/approach In total, 19 graduate students engaged in a CoP to facilitate social interactions, knowledge sharing and learning within a culture of scholarship. A descriptive qualitative research study was conducted using semistructured interviews with eight participants who had attended the CoP meeting. Findings All participants were from the School of Nursing and perceived the CoP to be beneficial, particularly international students who had challenges in adapting to new academic and social environments. Areas for improvement include creating a group structure that enhances belonging and learning. Originality/value This is the first CoP that was implemented at the Faculty of Health Sciences at the authors’ university. It has been the authors’ experience that a CoP can benefit graduate students through networking, knowledge sharing, social support and learning. The finding of this research will be used to inform a new CoP to address the needs of graduate students. The authors will be adapting the CoP to the current context that includes a virtual platform during the COVID-19 pandemic and will include content specific for international students.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".