Hospital restructuring stressors, work—family concerns and psychological well-being among nursing staff
Bibliographic record
Abstract
This study examined relationships between hospital restructuring and downsizing stressors, work-family and family-work conflict, job and family satisfaction and psychological well-being. Data were collected from 686 hospital-based nurses, the vast majority women, using anonymous questionnaires. Two research models hypothesizing both direct and indirect effects among these variables were tested using LISREL. Considerable support was found for these models. Restructuring and downsizing stressors had significant relationships with work-family conflict but not family-work conflict. Work-family conflict and family-work conflict, in turn, had significant relationships with psychological health. These results indicate that those responsible for the implementation of organizational restructuring and downsizing must be sensitive to the larger family and community effects of these initiatives. Fortunately, a growing body of literature on best practice provides considerable guidance on how to more effectively plan and manage these transitions. Este estudio examina relaciones entre estresores relacionados a la reestructuracion y la reduccion de hospitales, el conflicto trabajo-familia y el conflicto familia-trabajo, la satisfaccion laboral y familiar, y el bienestar sicologico. Utilizando cuestionarios anonimos, se recopilaron datos de 686 enfermeros, la gran mayoria de ellos mujeres, con base en hospitales, Se utilizo LISREL para poner a prueba dos modelos de investigacion que plantearon como hipotesis efectos directos e indirectos entre estas variables. Se encontro considerable apoyo para estos modelos. Los estresores, reestructuracion y reduccion, se relacionaron significamente con el conflicto trabajo-familia pero no con el conflicto familia-trbajo. A su vez, el conflicto trabajo-familia y el conflicto familia-trabajo se relacionaron significamente con el bienestar sicologico. Estos resultados indican que los responsables de la implementacion de la reestructuracion y reduccion organizativas deben estar muy conscientes de los efectos familiares y comunitarias de estas iniciativas. Afortunadamente, una literatura cada vez mayor sobre la mejor practica nos proporciona consejos importantes sobre como mejor planificar y dirigir estas transiciones.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".