Ideas for Extending the Approach to Evaluating Health in All Policies in South Australia Comment on "Developing a Framework for a Program Theory-Based Approach to Evaluating Policy Processes and Outcomes: Health in All Policies in South Australia"
Bibliographic record
Abstract
Since 2008, the government of South Australia has been using a Health in All Policies (HiAP) approach to achieve their strategic plan (South Australia Strategic Plan of 2004). In this commentary, we summarize some of the strengths and contributions of the innovative evaluation framework that was developed by an embedded team of academic researchers. To inform how the use of HiAP is evaluated more generally, we also describe several ideas for extending their approach, including: deeper integration of interdisciplinary theory (eg, public health sciences, policy and political sciences) to make use of existing knowledge and ideas about how and why HiAP works; including a focus on implementation outcomes and using developmental evaluation (DE) partnerships to strengthen the use of HiAP over time; use of systems theory to help understand the complexity of social systems and changing contexts involved in using HiAP; integrating economic considerations into HiAP evaluations to better understand the health, social and economic benefits and trade-offs of using HiAP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.064 | 0.080 |
| Insufficient payload (model declined to judge) | 0.005 | 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".