Latent profiles of nurses’ subjective well‐being and its association with social support and professional self‐concept
Bibliographic record
Abstract
AIM: To identify latent profiles of nurses' subjective well-being (SWB) and explore its association with social support and professional self-concept. DESIGN: This study used an online survey and cross-sectional latent profile analysis design. METHODS: A total of 1009 nurses from 30 hospitals in Guangdong Province, China, were selected using convenience sampling. An online questionnaire survey comprising the following scales was distributed: Index of Well-Being, Nurses' Professional Self-concept Questionnaire and Multidimensional Scale of Perceived Social Support. Nurses' SWB was examined and categorized into profiles using nine Index of Well-being items as explicit variables and ordinal logistic regression analysis was performed to explore factors related to the distinct categories. RESULTS: Nurses' SWB was divided into four latent profiles: extremely low, low, moderate and high. Regression analysis showed that social support and professional self-concept influenced SWB. There were statistically significant differences in age, title, working years, social support and professional self-concept among nurses in the different well-being categories. Ordered logistic regression analysis showed that social support and professional self-concept are associated with different SWB profiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".