Bibliographic record
Abstract
Advocacy is an evolving and underdeveloped element of public health practice. Historically, it was used to describe activities undertaken by persons on behalf of the poor, the sick or oppressed. In the seventies, led by tobacco control advocates such as Pertschuk in the United States, Gray in Australia and Daube in the United Kingdom, public health advocacy became more focused on structural and policy change. Since the Ottawa Charter (WHO, 1986), the health promotion movement has embraced a broader view of the role of advocacy. The public health community now see advocacy as social action primarily aimed at effecting changes in legislation, policy and environments that support healthy living. Advocacy is defined by the World Health Organization as a combination of individual and social actions designed to gain political commitment, policy support, social acceptance and systems support for a particular health goal or programme (WHO, 1995). This paper describes a model for understanding and mobilising physical activity advocacy. It outlines a three step process: 1. Gathering and translating the most pertinent physical activity evidence. Why advocate for physical activity? 2. Developing from the evidence, a physical activity advocacy agenda and articulating a plan (or plans) of key actions that will increase population levels of physical activity. What should be advocated? 3. Implementing a mix of advocacy strategies to influence and mobilise support for the physical activity agenda. How should advocacy be implemented?
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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".