Exploring nurses’ experiences transitioning from clinicians to professors at Ontario colleges
Bibliographic record
Abstract
BackgroundIn 2022, Ontario colleges and universities reported an estimated 67 vacant full-time nursing faculty positions, driving significant recruitment of nurses directly from clinical practice. Many of these nurses transition to academia lacking the necessary pedagogical preparation and socialization for a faculty role, leading to feelings of inadequacy, stress and an increased intent to leave their positions.ObjectiveThis qualitative descriptive study explored nurses' experiences as they transitioned into the professor role to identify strategies to decrease transition stress, improve career satisfaction, and decrease early-career nursing faculty attrition at Ontario colleges.MethodsData were collected in semi-structured interviews with nine participants from Ontario colleges offering the Bachelor of Science in Nursing degree and analyzed using Conventional Content Analysis.ResultsStudy findings detailed their emotional experiences, diverse preparations before becoming a professor, and the challenges navigating their new role. The study provided three major themes: 1) emotional aspects of the transition experience, 2) preparation for the nursing professor role, and 3) navigating the role and college setting. Nursing professors desired improved orientation programs, formal mentorship opportunities and socialization to the nursing professor role.ConclusionThe findings underscore the need for evidence-informed orientation programs that provide comprehensive training in institutional policies, nursing pedagogy, and support in adapting to the academic culture. These findings can guide Ontario colleges in offering standardized orientation programs that support nurses' excelling as professors and improve retention of this important group.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".