Mandatory internal mobility in French hospitals: the results of imposed management practices
Bibliographic record
Abstract
AIM: To describe the impact of a mandatory internal mobility policy on nurses working in French state-funded health establishments. BACKGROUND: Public hospitals in France rely on the internal mobility of nursing staff to respond to organisational needs, to reduce costs and to increase productivity. However, there is very little data on the impact of such management practices on the nurses themselves. METHOD: A cross-sectional study, including 3077 nurses from 35 hospitals in the region of Paris, was conducted. Data were collected using a validated self-assessment questionnaire. RESULTS: Forty per cent of French nurses are required to work in different units. This mobility differs according to individual characteristics [age (P = 0.04), length of service (P = 0.017)] and type of environment [hospital (P < 0.0001), specialty (P < 0.0001)]. CONCLUSION: We can distinguish two types of approaches for implementing a mandatory staff nurse mobility policy. The first is an event that is regular, planned and lasts for several days. The second is an event that is irregular, short and organised the day before or the day of the change. Overall, while nurses are dissatisfied with all types of mandatory unit changes, this dissatisfaction is primarily a result of the irregular mobility events. IMPLICATIONS FOR NURSING MANAGEMENT: This study demonstrates the importance of implementing a planned inter-unit mobility event and proposes recommendations for this type of implementation.
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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 | 0.004 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".