Generation‐specific incentives and disincentives for nurse faculty to remain employed
Bibliographic record
Abstract
AIMS: The aims of this paper are to: (1) describe work characteristics that nurse faculty report encourage them to remain in or leave their academic positions; and (2) determine if there are generational differences in work characteristics selected. BACKGROUND: Nurse faculty play key roles in preparing new nurses and graduate nurses. However, educational institutions are challenged to maintain full employment in faculty positions. DESIGN: A cross-sectional, descriptive survey design was employed. METHODS: Ontario nurse faculty were asked to select, from a list, work characteristics that entice them to remain in or leave their faculty positions. Respondent data (n = 650) were collected using mailed surveys over four months in 2011. RESULTS: While preferred work characteristics differed across generations, the most frequently selected incentives enticing nurse faculty to stay were having: a supportive director/dean, reasonable workloads, supportive colleagues, adequate resources, manageable class sizes and work/life balance. The most frequently selected disincentives included: unmanageable workloads, unsupportive organizations, poor work environments, exposure to bullying, belittling and other types of incivility in the workplace and having an unsupportive director/dean. CONCLUSION: This research yields new and important knowledge about work characteristics that nurse faculty report shape their decisions to remain in or leave their current employment. Certain work characteristics were rated as important among all generations. Where similarities exist, broad strategies addressing work characteristics may effectively promote nurse faculty retention. However, where generational differences exist, retention-promoting strategies should target generation-specific preferences.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".