A systematic review of the print media representation of ketamine treatments for psychiatric disorders
Bibliographic record
Abstract
Background Public and patient expectations of treatment influence health behaviours and decision-making. Aims We aimed to understand how the media has portrayed the therapeutic use of ketamine in psychiatry. Method We systematically searched electronic databases for print and online news articles about ketamine for psychiatric disorders. The top ten UK, USA, Canadian and Australian newspapers by circulation and any trade and consumer magazines indexed in the databases were searched from 2015 to 2020. Article content was quantitatively coded with a framework encompassing treatment indication, descriptions of prior use, references to research, benefits and harms, treatment access and process, patient and professional testimony, tone and factual basis. Results We found 119 articles, peaking in March 2019 when the United States Food and Drug Administration approved esketamine. Ketamine treatment was portrayed in an extremely positive light (n= 82, 68.9%), with significant contributions of positive testimony from key opinion leaders (e.g. clinicians). Positive research results and ketamine's rapid antidepressant effect (n= 87, 73.1%) were frequently emphasised, with little reference to longer-term safety and efficacy. Side-effects were frequently reported (n= 96, 80.7%), predominantly ketamine's acute psychotomimetic effects and the potential for addiction and misuse, and rarely cardiovascular and bladder effects. Not infrequently, key opinion leaders were quoted as being overly optimistic compared with the existing evidence base. Conclusions Information pertinent to patient help-seeking and treatment expectations is being communicated through the media and supported by key opinion leaders, although some quotes go well beyond the evidence base. Clinicians should be aware of this and may need to address their patients’ beliefs directly.
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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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".