COVID-19 impact on New Zealand general practice: rural-urban differences
Bibliographic record
Abstract
INTRODUCTION: In countries such as New Zealand, where there has been little community spread of COVID-19, psychological distress has been experienced by the population and by health workers. COVID-19 has caused changes in the model of care that is delivered in New Zealand general practice. It is unknown, however, whether the changes wrought by COVID-19 have resulted in different levels of strain between rural and urban general practices. This study aims to explore these differences from the impact of COVID-19. METHODS: This study is part of a four-country collaboration (Australia, New Zealand, Canada and the USA) involving repeated cross-sectional surveys of primary care practices in each respective country. Surveys were undertaken at regular intervals throughout 2020 of urban and rural general practices throughout New Zealand. Five core questions were asked at each survey, relating to experiences of strain, capacity for testing, stressors experienced, types of consultations being carried out and numbers of patients seen. Simple descriptive statistics were used to analyse the data. RESULTS: A total of 1516 responses were received with 20% from rural practices. A moderate degree of strain was experienced by general practices, although rural practices appeared to experience less strain compared to urban ones. Rural practices had fewer staff absent from work, were less likely to use alternative forms of consultations such as video consultations and telephone consultations, and had possibly lower reductions in patient volumes. These variations might be related to personal characteristics of rural as compared to urban practices or different models of care. CONCLUSION: New Zealand rural general practice appeared to have a different response to the COVID-19 pandemic compared to urban general practice, illustrating the significant strengths and resilience of rural practices. While different experiences from COVID-19 might reflect differences in the demographics of the rural and urban general practice workforce, another proposition is that this difference indicates a rural model of care that is more adaptive compared to the urban one. This is consistent with the literature that rural general practice has the capacity to manage conditions in a different way to urban. While other comparable countries have demonstrated a unique rural model of care, less is known about this in New Zealand, adding weight to an argument to further define New Zealand rural general practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".