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Pandemic-related experiences, mental health symptoms, substance use, and relationship conflict among older adolescents and young adults from Manitoba, Canada

2022· article· en· W4220925881 on OpenAlexafffundabout
Samantha Salmon, Tamara Taillieu, Janique Fortier, Ashley Stewart-Tufescu, Tracie O. Afifi

Bibliographic record

VenuePsychiatry Research · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsSubstance useMental healthPandemicPsychologyPsychiatryGerontologyCoronavirus disease 2019 (COVID-19)MedicineDisease

Abstract

fetched live from OpenAlex

There is growing awareness of the negative impact of the COVID-19 pandemic on young people. The purpose of this study was to examine older adolescents' and young adults' pandemic-related experiences, including financial difficulties, emotional support, social connections, mental health symptoms, substance use, and relationship conflict. Data from the Well-being and Experiences Study (The WE Study) were gathered from November to December 2020 in Manitoba, Canada, among a community sample (n = 664; ages 16-21 years). Over half of the sample self-reported increased stress/anxiety (57.6%) and depression (54.2%) attributed to the pandemic. Increased alcohol consumption was reported by 18.2% of alcohol-users. Among cannabis-users, 35.1% reported increased use. Conflict with parents, siblings, and an intimate partner increased for 19.9%, 15.2%, and 24.0% of respondents, respectively. Females reported greater financial burden, mental health burden, and conflict with parents than males. Young adults reported greater financial and mental health burden than older adolescents. Higher household income was protective of some experiences. The current study adds to growing evidence that young people were adversely impacted by the COVID-19 pandemic. Increased access to virtual support resources is needed and should continue following the pandemic. Evidence-based interventions may need to be tailored to females and young adults.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.098
GPT teacher head0.394
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2022
Admission routes3
Has abstractyes

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