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Record W4387241671 · doi:10.26635/6965.6066

Pacific patients' reasons for attending the emergency department of Counties Manukau for non-urgent conditions

2023· article· en· W4387241671 on OpenAlexaff
Vili Nosa, Catherine Yang, Vanessa Selak, David Schaaf

Bibliographic record

VenueNew Zealand Medical Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsAsia pacificEthnic groupAttendanceMedicinePacific islandersEmergency departmentFamily medicineNursingPolitical scienceBusiness

Abstract

fetched live from OpenAlex

AIM: To determine Pacific patients' reasons for Emergency Department (ED) use for non-urgent conditions by Pacific people at Counties Manukau Health. METHODS: Patients who self-presented to Counties Manukau ED with a non-urgent condition in June 2019 were surveyed. Responses to open-ended questions were analysed using a general inductive approach, in discussion with key stakeholders. RESULTS: Of 353 participants with ethnicity reported, 139 (39%) were Pacific, 66 (19%) Māori and 148 (42%) were non-Māori non-Pacific, nMnP. A total of 58 (42%) of Pacific participants had been to their general practitioner prior to presenting to the ED; this proportion was similar for Māori (19 [30%]) and nMnP (59 [40%]) (p=0.215). The most common reasons for ED attendance among Pacific (as well as other) participants were 1) advice by a health professional (41%, 95% CI 33-50%), 2) usual care unavailable (28%, 20-36%), 3) symptoms not improving (21%, 14-28%), and 4) symptoms too severe to be managed elsewhere (19%, 12-26%). CONCLUSIONS: Multiple reasons underlie non-urgent use of EDs by Pacific and other ethnic groups. These reasons need to be considered simultaneously in the design, implementation, and evaluation of multi-dimensional initiatives that discourage non-urgent use of EDs to ensure that such initiatives are effective, equitable, and unintended consequences are avoided.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.021
GPT teacher head0.323
Teacher spread0.302 · 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 designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

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