Individual and organizational features of a favorable work environment in nursing homes: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The organizational context in healthcare (i.e., the work environment) is associated with patient outcomes and job satisfaction. Long-term care is often considered to be a challenging work environment, characterized by high job demands, low job control, a fast work pace and job dissatisfaction, which may affect patient care and increase staff turnover.This study aims to investigate the organizational context in nursing homes and the features of favorable or less favorable work environments. METHODS: This study is a cross-sectional study of registered nurses and licensed practical nurses in Bergen, Norway (n = 1014). The K-means clustering algorithm was used to differentiate between favorable and less favorable work environments, based on the Alberta Context Tool. Multilevel logistic regression analysis was used to investigate the associations between individual sociodemographic factors, nursing home factors and the probability of experiencing a favorable work environment. RESULTS: 45% of the sample (n = 453) experienced working in a favorable work environment. Contextual features (especially a supportive work culture, more evaluation mechanisms and greater organizational slack resources) and individual features (having a native language other than Norwegian, working day shifts, working full time and belonging to a younger age group) significantly increased the likelihood of experiencing a favorable work environment. CONCLUSION: The work environment in nursing homes is composed of modifiable contextual features. Action in relation to less favorable features and their associated factors should be a priority for nursing home management. This survey indicates that specific steps can be taken to reduce the reliance on part-time workers and to promote the work environment among staff working the night shift.
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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 | 0.002 |
| 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 | 0.001 |
| 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".