A novel methodological approach to participant engagement and policy relevance for community-based primary medical care research during the COVID-19 pandemic in Australia and New Zealand
Bibliographic record
Abstract
Community-based primary care, such as general practice (GP) or urgent care, serves as the primary point of access to healthcare for most Australians and New Zealanders. Coronavirus disease 2019 (COVID-19) has created significant and ongoing disruptions to primary care. Traditional research methods have contributed to gaps in understanding the experiences of primary care workers during the pandemic. This paper describes a novel research design and method that intended to capture the evolving impact of the COVID-19 pandemic on primary care workers in Australia and New Zealand. Recurrent, rapid cycle surveys were fielded from May 2020 through December 2021 in Australia, and May 2020 through February 2021 in New Zealand. Rapid survey development, fielding, triangulated analysis and dissemination of results allowed close to real-time communication of relevant issues among general practice workers, researchers and policy-makers. A conceptual model is presented to support longitudinal analysis of primary care worker experiences during the COVID-19 pandemic in Australia and New Zealand, and key learnings from applying this novel method are discussed. This paper will assist future research teams in development and execution of policy-relevant research in times of change and may inform further areas of interest for COVID-19 research in primary care.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.473 | 0.390 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".