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Record W6931605367 · doi:10.5683/sp3/q4ulsx

Impact of Adverse Childhood Experiences and Resilience on Adult Emergency Room

2022· dataset· en· W6931605367 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
FieldComputer Science
TopicMachine Learning and Data Classification
Canadian institutionsQueen's UniversityWestern University
Fundersnot available
KeywordsAdverse Childhood ExperiencesResilience (materials science)Psychological resiliencePopulationEmergency departmentHealth careAdverse effectVulnerability (computing)

Abstract

fetched live from OpenAlex

Frequent users, or individuals with more than 4 emergency department (ED) visits in a year, constitute 10% of all ED users but account for 30% of all visits. Individuals with adverse childhood experiences (ACE) are known to have worse health than the general population and may be more likely to utilize EDs. Resilience, the ability to adapt to adversity, is a modifiable variable. Low resilience is associated with increased healthcare utilization. The relationship between ACE and resilience has not been examined in the ED. This study examines the impact of ACE on the frequency of ED use and how resilience modifies this relationship.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.177
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.286
Teacher spread0.276 · 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