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Record W4408821474 · doi:10.29173/cjen233

Frequent Mental health and Addiction related Emergency Department Visits: Perspectives from Healthcare Providers

2025· article· en· W4408821474 on OpenAlexaffvenue
Kristy Hoi Man Tang, Hua Li

Bibliographic record

VenueCanadian Journal of Emergency Nursing · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEmergency departmentAddictionMental healthcareMental healthHealth careMedical emergencyMedicineNursingPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Background: Rising mental health and addiction (MHA) related emergency department (ED) visits has been reported as a contributing factor for ED crises and increased healthcare costs. Studies on the issues have largely focused on patients’ perspective, while as another side of the phenomenon, healthcare providers’ (HCPs) experiences have been less researched. The purpose of this study was to explore the perspectives of HCPs on the issues. Methods: In this qualitative research, data collection involved semi-structured individual interviews. Thematic analysis approach was utilized in data analysis. Results: Six HCPs from diverse disciplines participated in this qualitative study. Four major themes emerged from the data analysis: (a) social determinants of mental health (housing crisis and financial problems); (b) structural barriers (overstimulation and not a priority in ED, inadequate knowledge and training among HCPs, lack of detox facilities, stigma from HCPs, shortages of HCPs); (c) suggestions for prevention (more funding/ resources and early childhood education) and (e) HCP’s response to working with patients (making a difference and rewarding). Conclusions: The study revealed that low socio-economic status and limited availability of community services were major factors for frequent MHA-related ED visits. All levels of governments, communities, and HCPs, especially nurses, should work together to better understand the complex needs of individuals with MHA disorders, and develop and implement effective interventions, ultimately reduce MHA-related ED recidivism.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.033
GPT teacher head0.401
Teacher spread0.368 · 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 designQualitative
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
Published2025
Admission routes2
Has abstractyes

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