Language and framing as determinants of the predominance of behavioural health promotion: an Australian view
Bibliographic record
Abstract
The language used in health promotion warrants attention as it shapes how health promotion is understood, constraining or opening up possibilities for action. The 2016 Shanghai Declaration and the 1986 Ottawa Charter for Health Promotion call for comprehensive approaches which include policy and environmental changes. Yet many health promotion programmes in Australia continue to focus on informational and/or behavioural strategies, and there is a contemporary tendency for such programmes to be described as 'sending messages'. This paper uses frame analysis to discuss the role of language, and specifically language that frames health promotion as sending messages, in contributing to and reinforcing the predominance of informational and/or behavioural strategies. It argues such 'message' language helps to set a pattern in which informational and/or behavioural strategies are assumed to be the primary goal and extent of health promotion; rather than one component of a comprehensive, multi-strategic approach. It discusses how frames can be 'taken for granted' and ways in which such frames can be challenged and broadened. It argues that the message frame and associated behavioural framings set narrow boundaries for health promotion, contributing to the continuation of health inequities. These frames can also displace the language of the Ottawa Charter, which has capacity to reframe health issues socio-ecologically and include collective strategies. The paper concludes that a first step (of the many needed) towards applying the Charter's approach and multi-level, multi-strategic framework is to use the innovative vocabulary it offers. The words matter.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".