Work engagement of hospital workers in times of pressure: do nonclinical hospital workers react differently from their well-studied clinical colleagues?
Bibliographic record
Abstract
Purpose While prevalence and value of nonclinical hospital workers, like quality or education professionals, increase, their work engagement is understudied. Work engagement of nonclinical and clinical hospital workers is critical considering the pressure of major challenges in healthcare. The pandemic was a natural experiment for this. Design/methodology/approach We conducted an observational survey study among all nonclinical and clinical hospital workers of the Jeroen Bosch Hospital, the Netherlands. In an employee satisfaction survey, we measured work engagement under acute pressure (just after the first COVID-19 wave in July 2020) and chronic pressure (within the second COVID-19 wave in November 2020) and to what extent psychological demands and co-worker support were related to work engagement. Findings For all hospital staff, “average” levels of work engagement were found under acute (response rate 53.9%, mean 3.94(0.81)) and chronic pressure (response rate 34.0%, mean 3.88(0.95)). Under acute pressure, nonclinical hospital workers scored lower on the subcategory dedication than clinical workers (mean 4.28(1.05) vs mean 4.45(0.99), p < 0.001). Under chronic pressure, no differences were found. For both nonclinical and clinical hospital workers, co-worker support was positively related to overall work engagement (beta 0.309 and 0.372). Psychological demands were positively related to work engagement for nonclinical hospital workers (beta 0.130), whereas in clinical hospital workers, psychological demands were negatively related to vigor (beta −0.082). Practical implications Hospitals face times of pressure. Fostering co-worker support under pressure may be vital for hospital management. Originality/value Work engagement of nonclinical hospital workers is understudied.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".