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Record W3121176099

Correlates of Preventable Emergency Department Visits in Canada: Evidence from the Literature and the Canadian Community Health Survey

2020· article· en· W3121176099 on OpenAlexaboutno aff
Tammy Lau

Bibliographic record

VenueScholarship@Western (Western University) · 2020
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmergency departmentEnvironmental healthMedicineMedical emergencyFamily medicineNursing
DOInot available

Abstract

fetched live from OpenAlex

Emergency department (ED) visits for primary healthcare-treatable conditions are preventable and indicate barriers to primary healthcare. The goal of this thesis was to explore the prevalence and key correlates of preventable ED visits among adults in Canada. Our systematic review found that the prevalence of these visits ranged from 4.3% to 59.1% and were associated with younger age, low education, low income, rural residence, and worse self-rated health. Our analysis of data from the 2015-2016 Canadian Community Health Survey found that 39.9% of adults with a regular healthcare provider considered their last ED visit to be preventable. In addition to age, education, and income, these visits were associated with being female, being employed, non-white ethnicity, having no recent consultations with a medical doctor, a strong sense of community belonging, and worse self-rated mental health. Future research should explore the healthcare experiences of these sub-populations to improve their access to care.

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)0.008
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.037
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.019
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
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 head0.108
GPT teacher head0.320
Teacher spread0.213 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2020
Admission routes1
Has abstractyes

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