Using the Job Demands-Resources Model to Underpin the Pandemic Nurses’ Turnover Intention Model to Examine Nurse Turnover Intentions in The Bahamas During the COVID-19 Pandemic: A Theory Paper
Bibliographic record
Abstract
Nurse turnover can affect the accessibility of healthcare services, quality of patient care, and nurse well-being. Various individual and contextual factors have been found to predict nurse turnover. A growing body of evidence now suggests the emergence of another potential predictor─fear related to the novel coronavirus, SARS-CoV-2 also known as COVID-19. To limit consequences, stakeholders must collaboratively develop empirically supported interventions to reduce nurse turnover. The purpose of this paper is to explain the novel use of the Job Demands-Resources (JD-R) model as a theoretical underpinning of the empirically supported Pandemic Nurses’ Turnover Intention (PNTI) model which is used to examine factors influencing nurses’ turnover intentions in The Bahamas during the COVID-19 pandemic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".