Recovery from the Pandemic: The Study of Fluctuations and Short-Term Forecasting in The Canadian Health System Workforce
Bibliographic record
Abstract
The healthcare industry is an area that should be meticulously planned to meet the needs of the population under any circumstances. Studying changes in the healthcare and social assistance workforce can provide direction and data support for these plans. However, at times, the factors influencing workforce changes are diverse and unpredictable. The grey system is a simple method used for handling incomplete data and conducting short-term forecasts. Its main advantages lie in its adaptability to small sample sizes and incomplete data, as well as its interpretability. Nearly all businesses were significantly impacted by the COVID-19 epidemic in 2020, but the healthcare industry was particularly heavily afflicted. By establishing a grey forecasting model based on data of the healthcare and social assistance industry workforce in Canada, this study aimed to simulate trends and predict future values of the industry’s labour force. The results indicate that the data has consistently grown at a relatively constant rate in the decade, with noticeable fluctuations in growth rate occurring during the COVID-19 pandemic. However, after 2021, the growth rate gradually returned to pre-pandemic levels.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".