Advice giving in a health emergency: a case study of the SARs website
Bibliographic record
Abstract
In the midst of the internet boom, there has been a growing interest in the use of the information communication technology (ICT) for the advancement of health. This led to IHC or ‘interactive health communication' (Robinson et al. 1998: 1264) which defined as ‘the interaction of an individual – consumer, patient, caregiver or professional with or through an electronic or communication technology devise to access or transmit health information, or to receive or provide guidance and support on health-related issue’. There is no denying the continued dramatic growth of the internet for health-related purposes. The general use of e-health sites grew 176% in 1999 much faster than the growth in general internet usage (Media Matrix 2000). Given the popularity and the relative infancy of the e-health technology, it is vital to ensure that the quality, effectiveness, affordability and accessibility of the e-health sites will have a positive impact on both the individual and public as a whole. The global outbreak of the Severe Acute Respiratory Syndrome (SARS) in 2003 provided an opportunity to study how e-health sites offer advice and information during a global health emergency. Thus, this study focuses on health webpages from three countries: China, Singapore and Canada which were worst hit by the disease. Advice giving has always been a central part of various health and medical settings. There is also ample evidence from previous research to show the problematic nature of offering and managing advice. The many implications of advice giving are at the heart of this study as the difficulties of offering advice and information becomes even more problematic when dealing with a disease like SARS. This disease threatens not only the health and well being of people but also the economic and social stability of the nation. The speed in which the disease had spread as well as the rapid rise in the death tolls posed a serious threat to a possibility of a global epidemic (CSR/WHO 2003). Such serious threats to the global public health makes it even more crucial for health professionals to provide information and advice to the public in order to reassure people that something is being done and can be done to control the situation.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".