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Record W4362669990 · doi:10.54097/ehss.v10i.6888

An Examination of The Impact of COVID-19 on the Mental Health and Substance Use of Social Minorities in Canada

2023· article· en· W4362669990 on OpenAlexaffabout
Jiayi Chen

Bibliographic record

VenueJournal of Education Humanities and Social Sciences · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMental healthPublic healthPsychiatryPandemicCannabisUnintended consequencesAnxietySubstance abusePolitical scienceMedicineEnvironmental healthCriminologyPsychologyBusinessCoronavirus disease 2019 (COVID-19)Nursing

Abstract

fetched live from OpenAlex

COVID-19 outbreak in Canada has resulted the implementation of public health mitigation laws regarding self-isolation, country-wide lockdowns, and closure of public services to decrease the spread of COVID-19. The application of these public health policies facilitated unintended consequences and side effects that would eventually intensify the already existing mental health issues and substance abuse prevalent amongst these vulnerable populations. People who use drugs (PWUD), wellness service providers, households with young children, and LGBTQ2+ people have reported their concerns for either food security, financial obstacles including the lack of job guarantee, limited access to public resources, and the ongoing opioid crisis in Canada that collided with the pandemic. These concerns have contributed greatly to the positive feedback cycle between the increasing use of substance including alcohol, cannabis, and opioids, and deteriorating mental health issues such as anxiety, post-traumatic syndrome, and depression. This study aims to discuss and compile the responses of social minorities in Canada regarding the use of substances and mental health conditions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.845

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.221
GPT teacher head0.470
Teacher spread0.250 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2023
Admission routes2
Has abstractyes

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