On the Combined Role of Work Engagement and Burnout Among Novice Nurses: A Longitudinal Person-Centered Analysis
Bibliographic record
Abstract
This study examined the profiles taken by global and specific facets of work engagement and burnout among a sample of novice ( M tenure = 3.77 years) nurses ( n = 570; 88.4% females; M age = 29.3 years). This study also investigated the role of psychological need satisfaction in the prediction of profile membership, and the implications of these profiles for attitudinal (job satisfaction), behavioral (in-role and extra-role performance, absenteeism, and presenteeism) and health (perceived health difficulties) outcomes. Latent profile analyses revealed six profiles: High Global Engagement and Low Global Burnout, Moderately High Global Engagement and Moderately Low Global Burnout, Low Dedication and Efficacy and Highly Cynical, Dedicated but Exhausted Burned-Out, Low Efficacy Burned-Out, and Very Low Global Engagement and Very High Global Burnout. Although these profiles were replicated over a 1-year period, profile membership was only weakly stable. The most beneficial outcomes were observed in the High Global Engagement and Low Global Burnout profile, and the most detrimental in the Very Low Global Engagement and Very High Global Burnout profile. Need satisfaction was also associated with profile membership, although associations were stronger for global levels of need satisfaction than for specific levels of autonomy, competence, and relatedness need satisfaction.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".