Enhancing the Public Health Nutrition Workforce: What Canada can learn from the Australian Experience
Bibliographic record
Abstract
Workforce development in the field of public health nutrition (PHN) is an important capacity building strategy required to effectively address local, national and globally recognized priority issues such as obesity, fruit and vegetable promotion and breastfeeding. As a strategy and focus of societal effort however, it receives little overt attention relative to its importance. Workforce development needs to look beyond the traditional focus on training and recognize that there are a range of determinants that influence the effectiveness of the workforce in achieving PHN goals. In simple terms, workforce development in PHN should include a combination of strategies that focus on workforce quantity (size and composition), quality (training and continuing professional development) and management. This presentation will draw on research conducted in Australia over the past 5 years that has highlighted the determinants of PHN workforce capacity and identified a framework for workforce development based on this analysis. It will share the lessons from Australia's experience including the challenges that this development presents to dietitians.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".