Impact of socioeconomic status on utilisation of a <scp>Virtual Emergency Department</scp>: An exploratory analysis
Bibliographic record
Abstract
OBJECTIVE: To explore whether utilisation of a Virtual Emergency Department (VVED) differs according to socioeconomic status (SES). METHODS: A retrospective analysis was undertaken of data from the VVED - a telehealth service that provides care for patients across Victoria, Australia with non-life-threatening emergencies. The study included all individuals who presented to the VVED between July 2022 and June 2023 through the two most common referral pathways (self-referral and ambulance referral). Area-level SES was ascertained by matching residential postcodes to the corresponding Australian Bureau of Statistics (ABS) Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD) decile. IRSAD scores were divided into quintiles (1 = lowest SES, 5 = highest SES) and multivariable logistic regression modelling was used to analyse associations between the SES quintile and referral pathway, presented as odds ratios (ORs) with 95% confidence intervals (CIs). RESULTS: There were 68 598 participants included in the analyses (mean age: 36.6 years; 58.4% female). Compared to SES quintile 3, higher odds of self-referral to the VVED were observed in the two most advantaged SES groups (Quintile 4; adjusted OR [aOR] = 1.16; 95% CI: 1.06-1.26; P = 0.001) (Quintile 5; aOR = 1.38; 95% CI: 1.25-1.52; P < 0.001). Conversely, lower odds of self-referral were observed in the most disadvantaged SES group (Quintile 1; aOR = 0.82; 95% CI: 0.75-0.90; P < 0.001). CONCLUSIONS: The present study demonstrated a relatively even utilisation of the VVED service across SES population groups. The use of healthcare provider pathways, such as ambulance paramedics, may increase equitable access to telehealth. Clinical attention should be directed toward specific social groups in the emergency care setting.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".