Demand Forecasting of Nurse Talents in China Based on the Gray GM (1,1) Model: Model Development Study
Bibliographic record
Abstract
BACKGROUND: In a global context, the shortage of nursing personnel has emerged as a significant challenge, particularly in countries such as China experiencing population aging. The inadequacy of nursing human resources has become one of the primary threats affecting the quality of health services available to Chinese residents. Therefore, forecasting the demand for nursing personnel has become an important issue. OBJECTIVE: This study presents a Gray GM (1,1) forecasting model for predicting the future 10-year demand for nursing workforce and the number of specialized geriatric nurses, aiming to provide a scientific basis for the development of policies in health care institutions in China. METHODS: Based on data from the China Statistical Yearbook 2022, the Gray GM (1,1) model was used to predict the demand for nursing jobs and geriatric nurses over the next 10 years (2024-2033). RESULTS: The results indicate that from 2024 to 2033, amidst a continuous growth in the overall population and an increasingly pronounced trend of population aging, the demand for nursing workforce in China, especially for specialized geriatric nurses, is projected to steadily increase. CONCLUSIONS: The paper provides a reference basis for the establishment of China's health care workforce system and the involvement of government departments in health care workforce planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".