Practice-based research networks: What they are and why Australia needs them
Bibliographic record
Abstract
A PRACTICE-BASED RESEARCH NETWORK (PBRN) is a group of primary care medical practices working together to undertake research of relevance to primary care.The first PBRNs likely developed in the UK 1 and Netherlands 2 in the late 1960s and in the USA in the 1970s. 3Similar groups formed in Canada in following years. 3Arguably, the earliest PBRN-like collaboration in Australia was established in 1962.At that time, the Research Committee of the (then) Australian College of General Practitioners organised 85 volunteer general practitioners (GPs) to collect data for one year on 174,000 patients in a national morbidity survey. 4The development of Australian PBRNs gained momentum in earnest with Commonwealth Primary Health Care Research Evaluation and Development (PHCRED) strategy funding from 2000 to 2011.Pirotta and Temple-Smith reported that over 20 Australian PBRNs received support during this period of dedicated funding. 3When functioning optimally, a PBRN is a collaborative learning community of academics and primary care clinicians, formed to generate, disseminate and integrate new knowledge in order to improve patient outcomes. 5PBRNs provide a mechanism for undertaking research in the community in order to inform community-based practice, rather than extrapolating findings from research based in tertiary centres.This offers the prospect of investigator-driven research,
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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 | 0.136 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.026 | 0.036 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".