Assessment and management of lifestyle risk factors in rural and urban general practices in Australia
Bibliographic record
Abstract
Prevention of cardiovascular disease is a major public health challenge. Many chronic health problems are amenable to lifestyle interventions, which can ameliorate progression of disease and contribute to primary prevention. Prior to a large randomised controlled trial we assessed preventive care in trial practices. General practitioners and practice nurses completed a preventive care questionnaire covering frequency of assessing and managing behavioural and physiological risk factors, which was developed from previously validated instruments. Factor analysis confirmed 10 scales. Scores for rural and urban respondents were contrasted using univariate statistics. Sixty-three general practitioners and practice nurses completed the questionnaire (27 urban and 36 rural). The clinicians reported high levels of assessment and advice for cardiovascular risk factors but less frequent referral. There were no differences between urban and rural practitioners in relation to assessment of risk or stage of change, referral or barriers to referral or management of high blood pressure. Rural practitioners had lower scores for frequency of advice, and management of obesity/overweight, pre-diabetes and high lipids. Although clinicians report frequently advising high risk patients to exercise more, there remain significant gaps in provision of dietary advice and referral. Greater attention to addressing these issues is required to maximise the potential benefits for cardiovascular disease prevention in general practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".