Analysis of print news media framing of ketamine treatment in the United States and Canada from 2000 to 2015
Bibliographic record
Abstract
OBJECTIVES: There are multifaceted views on the use of ketamine, a potentially addictive substance, to treat mental health problems. The past 15 years have seen growing media coverage of ketamine for medical and other purposes. This study examined the print news media coverage of medical and other uses of ketamine in North America to determine orientations and trends over time. METHODS: Print newspaper coverage of ketamine from 2000 to 2015 was reviewed, resulting in 43 print news articles from 28 North American newspapers. A 55-item structured coding instrument was applied to assess news reports of ketamine. Items captured negative and positive aspects, therapeutic use of ketamine, and adverse side effects. Chi-squares tested for changes in trends over time. RESULTS: In the 15-year reviewed period, the three most frequent themes related to ketamine were: abuse (68.2%), legal status (34.1%), and clinical use in anesthesia (31.8%). There was significant change in trends during two periods (2000-2007 and 2008-2015). In 2008-2015, print news media articles were significantly more likely to encourage clinical use of ketamine to treat depression (p = 0.002), to treat treatment resistant depression (p = 0.043), and to claim that ketamine is more effective than conventional antidepressants (p = 0.043). CONCLUSIONS: Our review found consistent positive changes in the portrayals of ketamine by the print news media as a therapeutic antidepressant that mirror the recent scientific publications. These changes in news media reporting might influence the popularity of ketamine use to treat clinical depression. Guidance is required for journalists on objective reporting of medical research findings, including limitations of current research evidence and potential risks of ketamine.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".