An Exploration of the Pre-tenure and Tenure Process Experiences of Canadian Nursing Faculty
Bibliographic record
Abstract
Background: The number of PhD-prepared nurses seeking employment in academia in Canada is not keeping up with the rate of retirements and the demands for new hires in the same settings. The current number of vacancies is expected to grow over the coming decade as an aging professoriate prepares to leave full-time employment. Retention of newly hired faculty will become a critical issue for administrators in an increasingly competitive environment. Purpose: The purpose of this mixed-methods study was to explore how organizational culture, mentorship, and the perceived level of psychological and structural empowerment are associated with one’s work environment among pre-tenure and newly tenured nursing faculty in Canada. Methods: This article reports the qualitative findings from the in-depth, semi-structured interviews conducted with 10 faculty volunteers after they completed an online survey. Results: Respondents overwhelmingly expressed a desire for a collegial and supportive working environment with clearly articulated policies and a transparent process for achieving tenure in academia. A healthy work environment was clearly identified as critical to the tenure process. Mentorship was identified as critical to creating a productive research culture.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".