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Record W4389569335 · doi:10.1177/26320770231200211

Ontario Adults’ Mental Health and Wellbeing During the First 16 Months of the COVID-19 Pandemic

2023· article· en· W4389569335 on OpenAlexaffabout
Katie J. Shillington, Leigh M. Vanderloo, Shauna M. Burke, Victor Ng, Patricia Tucker, Jennifer D. Irwin

Bibliographic record

VenueJournal of Prevention and Health Promotion · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsCollege of Family Physicians of CanadaUniversity of TorontoChildren’s Health Research InstituteHospital for Sick ChildrenWestern University
Fundersnot available
KeywordsMental healthPandemicCoronavirus disease 2019 (COVID-19)MedicinePsychologyRepeated measures designGerontologyPsychiatry

Abstract

fetched live from OpenAlex

This study quantitatively assessed adults’ mental health and overall wellbeing over time during the first 16 months of the pandemic in Ontario, Canada. A total of 2,188 participants participated in the study and completed online questionnaires at three time points (baseline—April–July 2020; time 2—July–August 2020; and time 3—July–August 2021), which included demographic questions, the Mental Health Inventory (MHI), and the Personal Wellbeing Index-Adult (PWI-A). One-way repeated-measures ANOVAs revealed a statistically significant increase over time in participants’ mental health (MHI), as well as a significant decrease in their satisfaction with their standard of living, physical health, mental health, personal relationships, safety, community-connectedness, future security, and spirituality/religion (PWI-A). While participants’ mental health improved, their mean scores indicated the presence of mental health disorders. Generally speaking, over the first 16 months of the pandemic, the self-reported mental health of Ontario adults improved, while their perceived wellbeing declined.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.109
GPT teacher head0.437
Teacher spread0.328 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

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