Chronic Disease Coverage in Canadian Aboriginal Newspapers
Bibliographic record
Abstract
PURPOSE: To determine the volume and focus of articles on four chronic diseases in newspapers targeting First Nations, Métis, and Inuit in Canada. METHODS: From a sampling frame of 31 Aboriginal newspapers published in English from 1996-2000, 14 newspapers were randomly selected allowing for national and regional representation. Newspaper archives were searched at the National Library of Canada and articles selected if the disease terms cancer, cardiovascular disease, diabetes, or HIV/AIDS appeared in the headline, or in the first or last paragraph of the article. Articles were coded for inclusion of mobilizing information (local, distant, unrestricted, not specified, none) and content focus (scientific, human interest, commercial, other). Cancer articles were categorized by tumor site specificity. Data were analyzed by frequency, cross tabulations, and chi-square analysis. RESULTS: Of 400 chronic disease articles, there were significantly more articles on HIV/AIDS (167 or 41.8%) and diabetes (135 or 33.8%) and few articles on cancer (56 or 14%) and cardiovascular disease (30 articles or 7.5%) (p<0.001). Slightly more than one third (36.5%) of the articles contained mobilizing information to enable readers to take further health action. Mobilizing information was virtually absent from cardiovascular (7/30 or 23%) and diabetes (29/135 or 21.5%) articles. Site specific cancer coverage differed significantly from chance (p<0.001) with 41% of the articles on breast cancer and no articles on lung or colorectal cancers. INTERPRETATION: Given the burden of tobacco-related cardiovascular disease and cancer in Canadian Aboriginal people, the lack of coverage and limited mobilizing information in ethnic newspapers are a missed opportunity for health promotion.
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 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".