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Record W2153314311

Frequent users of emergency departments. Do they also use family physicians' services?

2002· article· en· W2153314311 on OpenAlexaffabout
Benjamin T.B. Chan, Howard Ovens

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsMedicineEmergency departmentFamily medicinePrimary carePopulationObservational studyPsychosocialMedical diagnosisSocioeconomic statusMedical emergencyNursingPsychiatryEnvironmental healthInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether frequent users of emergency department (ED) services use more or fewer primary care services than other ED patients. DESIGN: Population-based, observational, cross-sectional study. SETTING: Province of Ontario in 1997-1998. PARTICIPANTS: Frequent users of EDs, defined as people with at least 12 ED physician assessments yearly, were compared with those with one to 11 assessments yearly. MAIN OUTCOME MEASURES: Number of general practitioner and family physician (GP/FP) office visits and number of GP/FPs visited; diagnoses made during office visits; referrals by GP/FPs to specialists. RESULTS: Three quarters of frequent users of EDs visited GP/FPs at least six times yearly, and more than half visited at least 12 times yearly. Although frequent users of EDs saw many GP/FPs (4.2 vs 1.6 in the control group, P < .001), they received, on average, 73% of their primary care from the GP/FPs whom they saw most frequently. Frequent users of EDs also had more referrals to specialists (4.0 vs 1.0). Frequent users of EDs were more likely to live in low socioeconomic neighbourhoods and to be diagnosed with psychosocial conditions (24.1% vs 11.1%). CONCLUSION: Most frequent users of EDs have periodic contact with primary care physicians. Communication and coordination of care between EDs and primary care settings could be easier than anticipated, because in most cases, frequent users of EDs seek most of their care from one main ED and one primary care physician.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.652

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 science0.0000.000
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 head0.043
GPT teacher head0.259
Teacher spread0.215 · 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

Citations101
Published2002
Admission routes2
Has abstractyes

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