Factors associated with the use of psychedelics, ketamine and MDMA among sexual and gender minority youths in Canada: a machine learning analysis
Journal of Epidemiology & Community Health,
Год журнала:
2024,
Номер
78(4), С. 248 - 254
Опубликована: Янв. 23, 2024
Background
Substance
use
is
increasing
among
sexual
and
gender
minority
youth
(SGMY).
This
increase
may
be
due
to
changes
in
social
norms
socialisation,
or
SGMY
exploring
the
potential
therapeutic
value
of
drugs
such
as
psychedelics.
We
identified
predictors
psychedelics,
MDMA
ketamine
use.
Methods
Data
were
obtained
from
1414
participants
who
completed
ongoing
longitudinal
2SLGBTQ+
Tobacco
Project
Canada
between
November
2020
January
2021.
examined
association
80
features
(including
sociodemographic
factors,
mental
health-related
factors
substance
use-related
factors)
with
past
year.
Random
forest
classifier
was
used
identify
most
associated
reported
these
drugs.
Results
18.1%
have
psychedelics
year;
21.9%
at
least
one
three
Cannabis
cocaine
strongly
any
drugs,
while
cannabis,
but
not
use,
psychedelic
Other
health
stigma-related
also
Conclusion
The
individuals
appeared
largely
driven
by
those
them
together
other
Depression
scores
top
10
illicit
suggesting
that
there
benefit
These
characteristics
should
further
investigated
future
studies.
Язык: Английский
Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia
PLoS ONE,
Год журнала:
2024,
Номер
19(7), С. e0306359 - e0306359
Опубликована: Июль 2, 2024
Sleep
is
critical
to
a
person's
physical
and
mental
health
there
need
create
high
performing
machine
learning
models
critically
understand
how
rank
covariates.
Язык: Английский
Bridging Classical Methodologies in Salmonella Investigation with Modern Technologies: A Comprehensive Review
Microorganisms,
Год журнала:
2024,
Номер
12(11), С. 2249 - 2249
Опубликована: Ноя. 7, 2024
Advancements
in
genomics
and
machine
learning
have
significantly
enhanced
the
study
of
Язык: Английский