A Prompt to the Web: The Media and Health Information Seeking Behaviour
Bibliographic record
Abstract
UNLABELLED: OBJECTIVE, DESIGN, SETTING AND PARTICIPANTS: The objective was to investigate media influence on consumers' health related behaviours. A cross-sectional survey of randomly selected adults (18+ years) residing in the Hunter Region of New South Wales Australia was conducted. The sample was selected using a combination of the white pages and random digit dialling. MAIN OUTCOME MEASURES: The proportions of respondents who recalled seeing or hearing about conditions or treatments in the media over the 12 months prior to interview (August 2009-August 2010) and their subsequent health related behaviour. RESULTS: Although most survey participants reported seeking health information from their doctors, around two-thirds of survey participants (551, 68.8%) recalled hearing, seeing or reading about one or more medical conditions (total = 1097 instances) in the mainstream media over the past 12 months. Almost 40% of respondents (307, 38.4%) stated that they had looked for more information about a condition as a result of hearing about it in the media, and most used the internet (269, 87.4%). More than a quarter of respondents (215, 26.9%) indicated that they had asked their doctor about a condition they had heard about in the media. Around half of those who asked their doctor (109, 50.6%) reported that their inquiry resulted in them receiving treatment, of whom almost half (53, 48.3%) reported being prescribed a medicine. CONCLUSION: The survey results show that consumers become aware of medicines through traditional media and then to learn more often turn to the internet where quality of information may be poor.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".