Access, Use, and Patient-Reported Experiences of Emergency Care During the COVID-19 Pandemic: Population-Based Survey
Bibliographic record
Abstract
BACKGROUND: An increase in the number of people presenting to emergency departments (EDs) is contributing to ED overcrowding. In the early stages of the COVID-19 pandemic, there was a significant reduction in the number of ED presentations in Australia, creating an opportunity to learn from patients' experiences of alternative management options. OBJECTIVE: The aim of this study is to report on the use and experience of health services by Australian adults experiencing a health issue during the COVID-19 pandemic for which they would have presented at an ED prior to the pandemic. METHODS: An online survey was conducted in May 2020. Reported health issues were categorized using an existing classification system. Data collected included demographics, care pathways, levels of concern at times of health issue and survey completion, and patient-reported experiences with care. RESULTS: A total of 1289 eligible respondents completed the survey. Almost 25% (309/1289) of respondents avoided an ED presentation, of which 58% (179/309) used an alternative form of health care and 42% (130/309) self-managed. Respondents making face-to-face or telehealth appointments with their general practitioner (GP) reported high levels of ED avoidance (135/286, 47%) and mostly positive experiences of care provided by GPs. A high proportion of those who self-managed reported high levels of concern at the time of completing the survey (42/130, 32%). CONCLUSIONS: Telehealth consultations with GPs may be a more promotable alternative to the ED beyond the COVID-19 pandemic, providing easier access to a doctor with access to patients' medical histories than an appointment for a face-to-face consultation. GP telehealth consultations may also address barriers to accessing health care for those with potentially the greatest need. The reported use and positive experiences with GP telehealth appointments should inform further research on their appropriateness as an alternative to the ED.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".