AHSCs as Health Policy Transfer: Some Emergent Evidence From Australia Comment on "Academic Health Science Centres as Vehicles for Knowledge Mobilisation in Australia? A Qualitative Study"
Bibliographic record
Abstract
This commentary discusses Edelman et al 2020's recent exploratory study of the early development of 4 Academic Health Services Centres (AHSCs) in Australia. AHSCs were originally invented in the United States, but have then diffused to the United Kingdom and Canada over the last decade or so and now to Australia so they are a good example of health policy transfer. They are dedicated to advancing more speedy knowledge translation (KT)/mobilization ('from bench to bedside') and also the more effective commercialization of scientific inventions. The commentary argues some interesting if preliminary findings are identified in their study. Its limitations will also be considered. Finally, suggestions for future research are made, including more cross national and comparative studies.
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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 | 0.030 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".