How do general practices respond to a pandemic? Protocol for a prospective qualitative study of six Australian practices
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has transformed healthcare systems worldwide. Primary care providers have been at the forefront of the pandemic response and have needed to rapidly adjust processes and routines around service delivery. The pandemic provides a unique opportunity to understand how general practices prepare for and respond to public health emergencies. We will follow a range of general practices to characterise the changes to, and factors influencing, modifications to clinical and organisational routines within Australian general practices amidst the COVID-19 pandemic. METHODS AND ANALYSIS: This is a prospective case study of multiple general practices using a participatory approach for design, data collection and analysis. The study is informed by the sociological concept of routines and will be set in six general practices in Melbourne, Australia during the 2020-2021 COVID-19 pandemic. General practitioners associated with the Monash University Department of General Practice will act as investigators who will shape the project and contribute to the data collection and analysis. The data will include investigator diaries, an observation template and interviews with practice staff and investigators. Data will first be analysed by two external researchers using a constant comparative approach and then later refined at regular investigator meetings. Cross-case analysis will explain the implementation, uptake and sustainability of routine changes that followed the commencement of the pandemic. ETHICS AND DISSEMINATION: Ethics approval was granted by Monash University (23950) Human Research Ethics Committees. Practice reports will be made available to all participating practices both during the data analysis process and at the end of the study. Further dissemination will occur via publications and presentations to practice staff and medical practitioners.
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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 | 0.100 | 0.070 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.055 | 0.012 |
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".