Reporting on the Opioid Crisis (2000-2018) – the Globe and Mail, Canada’s English Language Paper of Record
Bibliographic record
Abstract
Abstract Objectives: We aim to describe the general characteristics of how Canada’s newspaper of record – The Globe and Mail, reports on opioid-related news, the opioid crisis and its victims,and explore how Canadians’ perceptions of the opioid crisis could have developed over time.Methods: We searched The Globe and Mailbetween 2000 and 2018. We identified all articles related to the keyword “opioids”. Independently and in duplicate, reviewers extracted qualitative data from articles. The Social Representation Theory was used as a framework for understanding the how the opioid crisis is portrayed in Canada. Results:Our search yielded 554 relevant opioid articles.The number of articles peaked in 2009, 2012, and in 2016, coinciding with major developments in the epidemic. The language used in this discourse has evolved over the years and has slowly shifted towards less stigmatizing language. Content analysis of the articles revealed common social representations attributing blame to pharmaceutical companies, physicians, and foreign countries.It is easy to blame these collectives as this contributes to social representations thatare anchored in thepublic’s predisposed notions.Conclusions:Canadian coverage of the opioid crisis is focused on basic social representations and blame patterns towards a few collectives, a shift towards root causes of the opioid epidemic could positively influence the general public’s perception of the opioid crisis and help reap deeper understanding of the issue. Journalists face several obstacles to achieve greater focus and framing of the opioid crisis, a closer working relationshipbetween the media and the research community is needed.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.043 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".