Part-Time Nurse Faculty Intent to Remain Employed in Academia: A Cross-Sectional Study
Bibliographic record
Abstract
The purpose of this study was to test and refine a model of part-time nurse faculty intent to remain employed in the academic organization. Cross-sectional survey methods were used. A total of 282 part-time nurse faculty working in colleges or universities in Ontario, Canada were invited to participate. Survey instruments and items measured demographic, workplace, nurse responses to the workplace, and external variables. Correlation, multiple regression, and mediation analyses were conducted using data from 119 participants (47.6% response rate). Of the 19 variables hypothesized to affect intent to remain employed in the academic organization, seven influenced intent to remain. The resulting model indicated that the older the part-time nurse faculty member, the lower the level of intent to remain and the more years worked in the organization, the higher the level of intent to remain. The more opportunities perceived to exist outside of the employing organization, the higher the level of intent to remain. Additionally, the more satisfied part-time nurse faculty were with their job overall, the higher their level of intent to remain. In the workplace, the more support from the leader, the more formal or informal recognition received, and the more fair work procedures were perceived to be, the higher levels of part-time nurse faculty intent to remain employed in the academic organization, mediated by job satisfaction. Although age, organizational tenure, and external career opportunities are non-modifiable variables, deans and directors can encourage part-time nurse faculty to remain employed in their academic job by focusing on enhancing overall job satisfaction. Effective strategies may include formal or informal acknowledgement of good performance, consistent verbal and behavioural support, and implementation of procedural practices, such as performance evaluations and pay raises in a fair manner.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".