Analysis and Suggestions on the Content and Quantity of Health Communication Articles for “Shanghai Morning Post”—The “Shanghai Morning Post” Health Communication Articles Published in the First Quarter of 2015 as an Example
Bibliographic record
Abstract
本文收集《新闻晨报》2015年第一季度发表的所有健康传播类文章共313篇，文章内容中包括疾病防治知识类科普文章169篇，健康指导类科普文章32篇，疾病与健康相关的广告类文章112篇。利用传播学相关理论对样本的内容、数量、传播特色等方面进行归类与分析。研究结果提示《新闻晨报》健康传播类专栏文章具有数量较大、议程设置紧密结合受众需要、契合各相关健康日主题、内容侧重于常见多发病等特点。但存在偏科倾向、版面栏目名称设置无规律以及广告偏多等问题。在此基础上提出栏目规划以及内容设置方面的建议。 This paper collects a total of 313 health communication articles in the first quarter of 2015 in Shanghai Morning Post, including 169 disease knowledge articles, 32 healthy instruction articles and 112 articles of advertising articles. We use the theory of communication to the sample content, quantity, dissemination characteristics and other aspects of classification and analysis. The results show that the “Shanghai Morning Post” health communication column has a large number of articles, the agenda closely with the needs of the audience, fit the relevant health day theme, focusing on the characteristics of common diseases. But there are deficiencies that partial tendencies, layout column name set irregular and advertising too mach and so on. On this basis, we put forward the column planning and content settings recommendations.
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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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.022 | 0.033 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".