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Record W3154837252 · doi:10.12927/hcq.2021.26465

Promoting Safety: Behavioural Emergency Response during the COVID-19 Pandemic

2021· article· en· W3154837252 on OpenAlexaffvenue
Stephanie Pedrotti Lucchese, Daniela Bellicoso, Kien Dang, Ifat Witz

Bibliographic record

VenueHealthcare Quarterly · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsMental Health Research CanadaSt. Michael's Hospital
Fundersnot available
KeywordsPandemicHarmCoronavirus disease 2019 (COVID-19)Medical emergencyMedicineHealth careMental health2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Patient safetyBest practiceNursingPsychologyPsychiatryPolitical scienceVirology

Abstract

fetched live from OpenAlex

During the ongoing COVID-19 pandemic, patients with and without pre-existing mental health diagnoses will either be admitted to the hospital as patients under investigation for COVID-19 or patients positive for COVID-19. A safe and timely response is required for patients exhibiting escalating behaviours (e.g., responsive, agitated and/or aggressive behaviours) to prevent harm to the patient, nearby patients and staff. In this paper, we report on a new protocol that has been implemented throughout our institution to address Code White calls for escalating behaviours during the COVID-19 pandemic. This procedure uses a least restraint-based philosophy for the management of an escalating situation, involves the use of an interprofessional team of healthcare providers (including mental health clinicians) and security team members and accounts for the need for personal protective equipment. We believe that other hospitals could benefit from knowing about this approach as a strategy to improve patient care and diminish disease transmission.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.118
GPT teacher head0.440
Teacher spread0.322 · 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.

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

Citations4
Published2021
Admission routes2
Has abstractyes

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