Pacific patients' reasons for attending the emergency department of Counties Manukau for non-urgent conditions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".