How a Canadian program that helps educators 'thrive' not just 'survive' could help address Australia's childcare staff shortage
Bibliographic record
Abstract
On Wednesday, federal parliament passed Labor's bill to reduce childcare fees for many Australian families.More affordable childcare for families is great, but it will not solve all the issues in the sector. Schools are not the only ones with a teacher crisis. Early childhood services are also hit with chronic staff shortages.As of October, there were about 6,800 advertised positions for early childhood educators in Australia. The pandemic has not helped. There was a 40% increase in job ads between April 2021 and April 2022.Before COVID-19, there was about 30% annual turnover in the sector, and up to 45% in rural and remote areas. A 2021 union study of more than 3,800 educators revealed 74% said they wanted to leave the sector in the next three years. The top reasons for wanting to leave were excessive workload, low pay and feeling undervalued. This turnover can impact upon children's wellbeing, development and learning.To find out more about the challenges educators face, how it impacts upon their wellbeing and learn from other countries, our international study explored the experiences of early childhood educators around the world. This article looks at the Australian and Canadian components of the study.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".