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Record W4304112967 · doi:10.1155/2022/8778430

Change in Police Attendance at Overdose Events following Implementation of a Police Non-Notification Policy in British Columbia

2022· article· en· W4304112967 on OpenAlexafffundabout
Amiti Mehta, Jessica Xavier, Heather Palis, Amanda Slaunwhite, Sandra Jenneson, Jane A. Buxton

Bibliographic record

VenueAdvances in Public Health · 2022
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia HospitalBC Centre for Disease Control
FundersBritish Columbia Centre for Disease ControlMinistry of Health, British Columbia
KeywordsAttendanceCriminologyMedical emergencyPolitical sciencePsychologyMedicineLaw

Abstract

fetched live from OpenAlex

Introduction. Bystanders at overdose events often hesitate to call 911 due to fear of police involvement. To address this, in 2016, British Columbia Emergency Health Services (BCEHS) introduced a policy to not routinely inform police of overdose events. This study explores change in police attended overdose events after the policy was implemented. Methods. Data on police attended overdose events were derived from naloxone administration forms in BC’s Take-Home Naloxone (THN) kits returned before and after the policy change. Segmented regression was conducted to quantify change in police attended overdose events. Results. The average proportion of police attended overdose events pre-policy was 55.6% compared to 37.9% post-policy. The segmented regression model demonstrated a 0.98% (95% CI: (−1.70 to −0.26)) decline ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>p</a:mi> <a:mo>=</a:mo> <a:mn>0.01</a:mn> </a:math> ) in police attended overdose events each month following the policy. Conclusion. Our findings suggest that the BCEHS policy contributed to a decrease in police attended overdose events.

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)0.001
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 score0.060
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.053
GPT teacher head0.423
Teacher spread0.370 · 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

Citations6
Published2022
Admission routes3
Has abstractyes

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