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Record W3138693945 · doi:10.1186/s12889-021-11807-4

A multicenter study of short-term changes in mental health emergency services use during lockdown in Kitchener-Waterloo, Ontario during the COVID-19 pandemic

2021· article· en· W3138693945 on OpenAlexaffabout
Christopher Dainton, Simon Donato‐Woodger, Charlene H. Chu

Bibliographic record

VenueBMC Public Health · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of TorontoMcMaster UniversityGrand River Hospital
Fundersnot available
KeywordsMental healthMedicinePublic healthEmergency departmentOccupational safety and healthPandemicSuicide preventionPoison controlPsychiatryPopulationHealth carePoisson regressionMedical emergencyEnvironmental healthCoronavirus disease 2019 (COVID-19)NursingDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic and subsequent lockdown measures have led to increasing mental health concerns in the general population. We aimed to assess the short-term impact of the pandemic lockdown on mental health emergency services use in the Kitchener-Waterloo region of Ontario, Canada. METHODS: We conducted an observational study during the 6-month period between March 5 and September 5, 2020 using National Ambulatory Care Reporting System metadata from mental health visits to three regional Emergency Departments (ED); mental health and substance related police calls; and calls to a regional mental health crisis telephone line, comparing volumes during the pandemic lockdown with the same period in 2019. Quasi-Poisson regressions were used to determine significant differences between numbers of each visit or call type during the lockdown period versus the previous year. Significant changes in ED visits, mental health diagnoses, police responses, and calls to the crisis line from March 5 to September 5, 2020 were examined using changepoint analyses. RESULTS: Involuntary admissions, substance related visits, mood related visits, situational crisis visits, and self-harm related mental health visits to the EDs were significantly reduced during the lockdown period compared to the year before. Psychosis-related and alcohol-related visits were not significantly reduced. Among police calls, suicide attempts were significantly decreased during the period of lockdown, but intoxication, assault, and domestic disputes were not significantly different. Mental health crisis telephone calls were significantly decreased during the lockdown period. There was a significant increase in weekly mental health diagnoses starting in the week of July 12 - July 18. There was a significant increase in crisis calls starting in the week of May 31 - June 6, the same week that many guidelines, such as gathering restrictions, were eased. There was a significant increase in weekly police responses starting in the week of June 14 - June 20. CONCLUSIONS: Contrary to our hypothesis, the decrease in most types of mental health ED visits, mental health and substance-related police calls, and mental health crisis calls largely mirrored the overall decline in emergency services usage during the lockdown period. This finding is unexpected in the context of increased attention to acutely deteriorating mental health during the COVID-19 pandemic.

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)0.002
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 score0.038
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.157
GPT teacher head0.420
Teacher spread0.263 · 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

Citations15
Published2021
Admission routes2
Has abstractyes

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