Communicating Medical Information Online. The Case of Adolescent Health Websites
Bibliographic record
Abstract
In recent times, our understanding and practice of public health has been increasingly guided by technological advances generally based on governmental decisions (Green et al. 2009). Not only does the growth of a public system for protecting health hinge upon scientific discovery and dissemination of medical knowledge, but also the World Wide Web has considerably changed the health communication environment. This paper considers the online health information addressed to adolescents. Given that young people have difficulty accessing traditional health services, in theory, the Internet might offer them a more confidential and convenient access to an unprecedented level of information about a diverse range of subjects (Hansen et al. 2003). In this context, the analysis concentrates on ‘adolescent health,’ and compares and contrasts the discourse of three websites: Healthdirect, a free service supported by the Government of Australia, SAHM managed by a multidisciplinary society based in the USA, Canada and the UK, and TeenMentalHealth.Org managed by the WHO (World Health Organization) Collaborating Centre in Mental Health Policy and Training. The study is designed to highlight both the specificities of communication of ‘adolescent health’ (Harvey 2014; Gotti, Maci and Sala 2015; Garzone and Ilie 2014), and the linguistic/discoursal and visual strategies adopted over the dedicated online platforms. Given the communicative immediacy of the new medium and the specificities of the target audience, it becomes crucial to see how the selected websites both linguistically and visually communicate medical information to adolescent web-users (LeVine and Scollon 2004; Kolucki and Lemish 2011).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.016 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.088 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".