Exploring the Transitional Experiences of Nursing Faculty at Ontario Colleges
Bibliographic record
Abstract
The global nursing shortage creates a challenge for patient care and increases the demand for qualified nursing faculty to train future nurses. In 2022, there were an estimated 67 vacant full-time faculty positions in Ontario colleges and universities, driving the recruitment of nurses from clinical practice (CASN, 2022). The transition from clinical practice to academia can be challenging. Although the transition experience of clinical nurses to academia has been examined in the literature, the perspective of Ontario college faculty still needs to be addressed. To address this gap, this qualitative descriptive study, informed by Transitions Theory, explored the experiences of novice college nursing faculty. Nine participants from Ontario colleges offering the Bachelor of Science in Nursing degree were interviewed. The study provided three themes: 1) emotional aspects of the transition experience, 2) preparation for the nursing professor role, and 3) navigating the role and college setting. The study found that transitioning into the nursing professor role is an emotional journey. Many novice nursing professors felt unprepared for their new roles and drew upon their clinical experiences to support their transitions. Finally, their ability to navigate their new role was impacted by the formal and informal support they received through orientation programs, mentorship, and socialization. These findings can guide academic leaders at Ontario colleges to offer standardized orientation programs that support nurses to excel as professors and improve retention of this important group. By increasing the retention of novice nursing professors, Ontario colleges can continue to educate future nurses to meet the province’s growing healthcare needs. Without qualified faculty, nursing schools are limited in their enrolment of students, resulting in fewer new nurses supporting patient care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| 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".