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Record W4380886258 · doi:10.1186/s13033-023-00585-8

Impact of COVID-19 on the lives of people with severe mental illness—front-line community psychiatry workers observation from a provincial survey of assertive community treatment teams in Ontario, Canada

2023· article· en· W4380886258 on OpenAlexaffabout
Michaela Beder, Saadia Sediqzadah, Matthew N. Levy, Madeleine Ritts, John Maher, Nicole Kirwan, Samuel Law

Bibliographic record

VenueInternational Journal of Mental Health Systems · 2023
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of TorontoCanadian Mental Health AssociationSt. Michael's Hospital
Fundersnot available
KeywordsMental illnessFront lineLonelinessPsychiatryAssertive community treatmentOutreachThematic analysisMental healthMedicineTherapeutic communityPsychologyNursingQualitative researchSociologyPolitical science

Abstract

fetched live from OpenAlex

Using an online survey distributed to members of the provincial organization that represents the 88 Assertive Community Treatment (ACT) and Flexible ACT teams in Ontario, Canada, this descriptive study relied on the unique vantage points and observations of the front-line community psychiatry workers who maintained contact with patients through outreach and telecommunication during the height of COVID-19. The patients who suffer from serious mental illness (SMI) were uniquely affected by COVID-19 due to the changes, reduction or shut down of many essential clinical and community support services. Thematic and quantitative analyses of the workers' observations highlighted 6 main areas of note, including significant social isolation and loneliness, clinical course deterioration and life disruption, increased hospital and ER use, police and legal contacts, and substance abuse and related deaths. There were also encouraging signs of positive adaptations in terms of independence and resilience. Reflections of these impacts and potential ameliorating approaches are further discussed.

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)0.002
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 score0.022
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.085
GPT teacher head0.401
Teacher spread0.316 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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