Moderating Effect of Age and Supervisory Status on Telework and Job Satisfaction Among Federal Employees
Bibliographic record
Abstract
Peer feedback benefits nursing education and practice by fostering collegial relationships and promoting the quality of care, yet students often lack training to engage effectively. While existing research has examined student perspectives, the experiences and strategies employed by nursing faculty remain underexplored. The purpose of this qualitative study, guided by the student feedback literacy framework, was to explore the perspectives of nursing faculty on the barriers, opportunities, and strategies for developing peer feedback skills among undergraduate nursing students. Seventeen Canadian nursing faculty participated in semi-structured virtual interviews. Analysis revealed five themes that aligned with the study’s three foci: strategies (a) teaching strategies and structural support; (b) shaping peer feedback practices; (c) learning environment and relational dynamics; (d) role modelling and professional socialization; and (e) challenges and barriers to feedback engagement. Findings revealed that peer feedback development is a collaborative process, requiring students’ active engagement alongside faculty guidance. A structured and formally taught approach that is responsive to student diversity, fosters safe learning environments, and normalizes feedback through culture and role modelling was emphasized. Future research could examine the perspectives of new graduate nurses, who stand at the intersection of education and practice. Nursing students who gain confidence in giving and receiving peer feedback are better prepared for reflective practice, effective communication, and safe, independent clinical work and ultimately contributing to positive social change.
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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 | 0.003 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".