Developing community-level social marketing messages to raise awareness of asthma in older Australians: preliminary results
Bibliographic record
Abstract
Background: While asthma awareness campaigns are generally aimed at children and their parents, asthma affects a similar proportion of older adults, often with more severe health consequences. Adults aged 55 years and over often have misconceptions about the severity of asthma and their likelihood of developing the disease. A targeted asthma awareness campaign utilising social marketing techniques could benefit the health outcomes and quality of life of this population.\nObjective: We aimed to pilot test our survey in the older adult population and to learn more about older adult’s asthma perceptions.\nMethods: One-hundred and fifteen adults aged 55 years and over completed a self-report survey about their asthma knowledge, beliefs and perceptions.\nResults: Preliminary results reveal that the majority of older adults do not think that they are susceptible to developing asthma. In terms of perceived severity, almost all respondents answered that it was serious. On the basis of these asthma beliefs, the audience was segmented into four groups.\nConclusions: Understanding older adults’ perceived susceptibility and severity allowed the segmentation of the audience according to health beliefs and perceptions about asthma. This has useful implications for message development and specific proposed health behaviours for each group.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".