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Record W4386502939 · doi:10.7870/cjcmh-2023-016

The Impact of the Covid-19 Pandemic on Assertive Community Treatment Team Functions, Clinical Services, and Observable Outcomes—A Provincial Survey in Ontario, Canada

2023· article· en· W4386502939 on OpenAlexaffvenueabout
Michaela Beder, John Maher, Saadia Sediqzadah, Nicole Kirwan, Madeleine Ritts, Matthew N. Levy, Samuel Law

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsCanadian Mental Health AssociationUniversity of Toronto
Fundersnot available
KeywordsAssertive community treatmentThematic analysisPsychosocialCoronavirus disease 2019 (COVID-19)PandemicMental healthMental illnessNursingPsychologyMedicineQualitative researchPsychiatrySociology

Abstract

fetched live from OpenAlex

Assertive Community Treatment (ACT) teams provide the most intensive care for patients with serious mental illness. This online study surveyed the 88 ACT and Flexible ACT (FACT) teams in Ontario, Canada (144/232 surveys, 62.1%) during the height of Covid-19 in 2021, and qualitative thematic analysis on impact of team function and patient outcome showed challenges of teams switching to virtual care, reduced psychosocial services, division to smaller groups, staff redeployment, having unequal compensations, and exacerbated regional differences; there were also increased patient stress, loneliness, hospital and ER visits, substance use and related deaths, and police/crisis team involvement. There was limited morbidity related to actual Covid-19; and positive adaptations included observed independence and resilience, increased interdependence with community partners, and new communication formats.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.052
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.213
GPT teacher head0.447
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2023
Admission routes3
Has abstractyes

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