Prescribed Antibiotics? Public Health Infographic
Bibliographic record
Abstract
An essential part of antibiotic stewardship is the type of media and visuals used to convey the intended messages to the target audience (Langdridge et al., 2019). Visual materials are important non-human actors through which “things medical” shape popular culture, and popular culture reciprocally influences the medical sphere (Clarke 2010, p. 104). A report by the Council of Canadian Academies (2019) stresses that rising antibiotic resistance will lead to detrimental impacts on Canadians’ quality of life and social connectivity. Informed by the digital trends of health-seeking behaviour, and notions of risk, responsibility, and lifestylisation characteristic of the biomedicalization era, the infographic presented here was designed to provide Canadians with a concise overview of proper antibiotic practices and its benefits, such as getting better sooner and reducing the potential for antibiotic resistance to grow (Clarke, 2010; Lucivero & Prainsack, 2015; Turner, 1997). Sociologists, behavioural scientists and public health professionals should collaborate to produce campaigns that utilize the advantages of digital spaces such as social media, with a focus on visual materials such as infographics (Ashiru-Oredope & Hopkins, 2015; Langdridge et al., 2019).
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.091 | 0.010 |
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".