Could pets be of help in achieving health literacy? A media analysis demonstration study
Bibliographic record
Abstract
This paper asks whether, when seeking to reach the public, interest in the health of pets merits consideration. Our data set consisted of 128 items from Canadian media coverage, 1996-2006, that dealt with bovine spongiform encephalopathy (BSE) as well as with cats, dogs or both. Three main messages regarding pet health and human health were identified: 'do not worry', 'do worry' and 'be cautious'. A minority of articles did not convey a pet health message or a human health message (6%), and contradictory messages regarding human and animal health frequently occurred (32%). While we did not assess how members of the public actually received or interpreted these messages, media coverage dealing with pets does appear to have the potential to influence people. Media reports of British cats being harmed by BSE, in fact, may have influenced public views worldwide. Thus, professionals should give careful consideration to pets when conveying health information. Nevertheless, we do not suggest pet health information substitute for human health information. Rather, interest in pets may provide an opportunity to complement and to reinforce communication about human health.
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".