Using network analyses to characterise Australian and Canadian frequent attenders to the emergency department
Bibliographic record
Abstract
OBJECTIVE: To explore and compare the characteristics of frequent attenders to the ED at an Australian and a Canadian tertiary hospitals by utilising a network analysis approach. METHODS: We conducted a retrospective population-based study using administrative data over the 2018 and 2019 calendar years. Participants were from a tertiary hospital in Melbourne, Australia, and Toronto, Canada. Frequent attenders were defined as patients with four or more visits in 12 months. Characteristics of younger (18-39 years), middle-aged (40-69 years) and older (70 years and older) frequent attenders were described using descriptive statistics and network analyses. RESULTS: Younger frequent attenders were characterised by mental illness and substance use, while older frequent attenders had high rates of physical (including chronic) diseases. Middle-aged frequent attenders were characterised by a combination of mental and physical illnesses. These findings were observed at both hospitals. Across all age groups, the network analyses between the Melbourne and Toronto hospitals were different. Among older frequent attender visits, more diagnoses were associated with high triage acuity at the Toronto hospital than at the Melbourne hospital. Some associations were similar at both sites, for example, the negative correlation between high triage acuity and joint pain. CONCLUSION: Younger, middle-aged and older frequent attenders have distinct characteristics, made readily apparent by using network analyses. Future interventions to reduce ED visits should consider the heterogeneity of frequent attenders who have needs specific to their age, presenting problems and jurisdiction.
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 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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".