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Record W4392479839 · doi:10.2196/46029

Ambulance Services Attendance for Mental Health and Overdose Before and During COVID-19 in Canada and the United Kingdom: Interrupted Time Series Study

2024· article· en· W4392479839 on OpenAlexaffvenueabout
Graham Law, Rhiannon Cooper, Melissa Pirrie, Richard Ferron, Brent McLeod, Robert Spaight, A Niroshan Siriwardena, Gina Agarwal

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsHamilton Health SciencesRegional Municipality of NiagaraNiagara CollegeMcMaster UniversityImpact
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Mental healthAttendanceInterrupted Time Series AnalysisPandemicInterrupted time series2019-20 coronavirus outbreakMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Medical emergencyPsychiatryPublic healthPsychologyNursingPsychological interventionVirologyOutbreakPolitical science

Abstract

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BACKGROUND: The COVID-19 pandemic impacted mental health and health care systems worldwide. OBJECTIVE: This study examined the COVID-19 pandemic's impact on ambulance attendances for mental health and overdose, comparing similar regions in the United Kingdom and Canada that implemented different public health measures. METHODS: An interrupted time series study of ambulance attendances was conducted for mental health and overdose in the United Kingdom (East Midlands region) and Canada (Hamilton and Niagara regions). Data were obtained from 182,497 ambulance attendance records for the study period of December 29, 2019, to August 1, 2020. Negative binomial regressions modeled the count of attendances per week per 100,000 population in the weeks leading up to the lockdown, the week the lockdown was initiated, and the weeks following the lockdown. Stratified analyses were conducted by sex and age. RESULTS: Ambulance attendances for mental health and overdose had very small week-over-week increases prior to lockdown (United Kingdom: incidence rate ratio [IRR] 1.002, 95% CI 1.002-1.003 for mental health). However, substantial changes were observed at the time of lockdown; while there was a statistically significant drop in the rate of overdose attendances in the study regions of both countries (United Kingdom: IRR 0.573, 95% CI 0.518-0.635 and Canada: IRR 0.743, 95% CI 0.602-0.917), the rate of mental health attendances increased in the UK region only (United Kingdom: IRR 1.125, 95% CI 1.031-1.227 and Canada: IRR 0.922, 95% CI 0.794-1.071). Different trends were observed based on sex and age categories within and between study regions. CONCLUSIONS: The observed changes in ambulance attendances for mental health and overdose at the time of lockdown differed between the UK and Canada study regions. These results may inform future pandemic planning and further research on the public health measures that may explain observed regional differences.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.023
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.008
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
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 head0.045
GPT teacher head0.387
Teacher spread0.342 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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