Evidence map›Paper›PMID 36168378›Full record

ArticleCureus2022

Differences in Attitudes Toward Medical Cannabis With Humanized Patient Scenarios.

Thomas A Clobes, Mya Arellano, Matin Gagnon, Colby Klaiman

Abstract read
In one paragraph

Article in Cureus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Thomas A ClobesHealth Sciences, California State University Channel Islands, Camarillo, USA.
Mya ArellanoHealth Sciences, California State University Channel Islands, Camarillo, USA.
Matin GagnonPsychology, California State University Channel Islands, Camarillo, USA.
Colby KlaimanBiology, California State University Channel Islands, Camarillo, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Attitudes toward medical cannabis are shaped by a number of factors, such as religion, previous use, and political affiliation. Individuals with less supportive beliefs toward cannabis, in general, may be more open to its therapeutic applications in a humanized patient scenario (PS). Methods A modified medical cannabis attitude scale was used to measure participants' attitudes toward medical cannabis. Two humanized patient scenarios were presented to the participants, and their level of agreement with the patient having access to medical cannabis was measured. After the scales were standardized, a Wilcoxon signed-rank test (WSR) was utilized to determine whether a difference between medical cannabis attitudes and approval of medical cannabis use in the humanized PS exists. Results A total of 645 participants completed the full survey and were included in the data analysis. Most participants were supportive of the patients in the humanized scenarios having access to medical cannabis; 76.1% and 75.7% of respondents, in each of the scenarios, selected the highest level of approval. There was a significantly higher approval for medical cannabis with the PS than attitudes toward medical cannabis in general (Z=-17.415, p<0.0005). Conclusion Individuals were more supportive of patient access to medical cannabis when exposed to humanized PS than their general attitudes toward medical cannabis indicated. Applying the results of this current research, a viable plan to reduce the stigma surrounding cannabis is to use patient testimonials in public-facing advocacy efforts regarding medical cannabis.

Indexed as

biasmedical cannabispsychoactive drugpublic healthstigmastigma in health care

Identifiers

PMID36168378
PMCPMC9507077

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.