Consequences of early career nurse burnout: A prospective long-term follow-up on cognitive functions, depressive symptoms, and insomnia
Bibliographic record
Abstract
BACKGROUND: Burnout is common among nurses and midwives. We examined whether an early career episode of burnout has long-term consequences on; a) cognitive functions, b) symptoms of depression, and/or c) insomnia for nurses a decade after graduation. METHODS: Symptoms of burnout were investigated in an observational longitudinal study of three national cohorts of registered nurses (RNs). Nursing students were recruited from all 26 of Sweden's nursing programs. Burnout was subsequently measured through annual assessment over the first three years post graduation, with one long-term follow-up 11-15 years after graduation. A total of 2474 nurses (62%) consented to participate at follow-up. Burnout was measured using items from the Oldenburg Burnout Inventory, cognitive function by a study specific instrument, depressive symptoms by the Major Depression Inventory, and sleep problems using items from the Karolinska Sleep Questionnaire. We used logistic regression to identify factors associated with consequences of early career burnout, adjusting for concurrent levels at follow up. FINDINGS: The prevalence of nurses reporting high levels of burnout symptoms at least one of the first three years of working life was 299 (12·3%). High levels of burnout symptoms in early working life were significantly related to more frequent symptoms of cognitive dysfunction, depression, and impaired sleep a decade later when taking current burnout levels into account. After controlling for both current symptoms of burnout and the other outcome variables, nurses with early career burnout still reported more frequent problems with cognitive functions and sleep but not depression. INTERPRETATION: The results of this study show that the detrimental processes caused by overwhelming or chronic stress start early on in nurses' careers and thus preventive efforts should preferably be introduced early on (e.g. as part of nursing education and onboarding programs). FUNDING: AFA Insurance Grant [number 150284].
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".