Bibliographic record
Abstract
The use of internet by the consumer for health information on obesity, weight management and other obesity related diseases continues to soar.The need for information has formed a health informatics sub-specialty referred to as Consumer Health Informatics.Obesity is just one of many health conditions where consumer education for empowerment is important to improve their own health.In 2012, the percentage of obese adults 18 and older was 27.7 % in US nationally.Unfortunately, by 2014 the US percentage of obese adults increased to 28.9 %.The obesity trends in Canada are similar to those of the United States.In 2012, Statistics Canada has reported that 18.4% of Canadians aged 18 and older have been reported as being obese.The most recent Canadian statistics from 2014 has reported 20.2% of Canadians aged 18 and older as obese.In today's technological society there are additional new abilities and skills required for health literacy.Computer literacy skill is required to assess, understand, and apply health information obtained through the internet [5].Being able to assess the information as to its reliability and accuracy is critical vs. assessing the sophisticated design of any given webpage.In my opinion, in effort to assist consumers searching for health in regards to health literacy and computer literacy skills the US government should promote and place more awareness on health information websites.Therefore, my recommendation for consumers is to follow the following steps by visiting ".gov", ".edu".and ".org" sites first prior to searching the less reliable and accurate ".com" websites for health information online.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.006 |
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".