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. 2020 Feb 21;15(2):e0229067.
doi: 10.1371/journal.pone.0229067. eCollection 2020.

Self-reported negative outcomes of psilocybin users: A quantitative textual analysis

Bheatrix Bienemann  1 , Nina Stamato Ruschel  1 , Maria Luiza Campos  1 , Marco Aurélio Negreiros  1 , Daniel C Mograbi  1   2
Affiliations

Self-reported negative outcomes of psilocybin users: A quantitative textual analysis

Bheatrix Bienemann et al. PLoS One. .

Abstract

Psilocybin, a substance mainly found in mushrooms of the genus psilocybe, has been historically used for ritualistic, recreational and, more recently, medicinal purposes. The scientific literature suggests low toxicity, low risk of addiction, overdose, or other causes of injury commonly caused by substances of abuse, with growing interest in the use of this substance for conditions such as treatment-resistant depression. However, the presence of negative outcomes linked to psilocybin use is not clear yet. The objective of this study is to investigate the negative effects of psilocybin consumption, according to the users' own perception through self-reports extracted from an online platform. 346 reports were analyzed with the assistance of the IRAMUTEQ textual analysis software, adopting the procedures of Descending Hierarchical Classification, Correspondence Factor Analysis and Specificities Analysis. The text segments were grouped in 4 main clusters, describing thinking distortions, emergencies, perceptual alterations and the administration of the substance. Bad trips were more frequent in female users, being associated with thinking distortions. The use of multiple doses of psilocybin in the same session or its combination with other substances was linked to the occurrence of long-term negative outcomes, while the use of mushrooms in single high doses was linked to medical emergencies. These results can be useful for a better understanding of the effects of psilocybin use, guiding harm-reduction initiatives.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Dendogram with the 25 words with highest χ2 in each cluster.
Small (red), medium (blue) and large (green) effect sizes, according to (30).
Fig 2
Fig 2. Relationship between clusters and words in each cluster.
Red–Cluster 1; green–Cluster 2; blue–Cluster 3; purple–Cluster 4.
Fig 3
Fig 3. Words relation by cluster and category associated with each cluster.
Words in blue belong to Cluster 1 that relates to category 3 (Bad Trips), words in green to Cluster 2 that relates to category 2 (Train Wrecks and Trip Disasters), words in red belong to Cluster 4 that relates to category 1 (Health Problems). Cluster 3 did not relate to any category and is not represented in the graph.
Fig 4
Fig 4. Co-occurrence and communities for words in cluster 1.
Fig 5
Fig 5. Co-occurrence and communities for words in cluster 2.
Fig 6
Fig 6. Co-occurrence and communities for words in cluster 3.
Fig 7
Fig 7. Co-occurrence and communities for words in cluster 4.
See this image and copyright information in PMC

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