The effect of a 12-week exercise and lifestyle management programme on cardiac risk reduction: A pilot using a kaupapa Māori philosophy
Bibliographic record
Abstract
Introduction: Cardiovascular disease remains the leading cause of premature death and disability for all New Zealanders. Māori, the Indigenous people of New Zealand, are disproportionately affected. The New Zealand Māori Health Strategy recognises that “health and wellbeing are influenced and affected by the ‘collective’ … and the importance of working with people in their social contexts, not just with their physical symptoms” (Ministry of Health, 2002, p. 1). In a Māori worldview, a holistic approach to health is innate. Objectives: This project piloted a kaupapa Māori approach within an existing 12-week clinical exercise and lifestyle management programme. The aims of the study were to determine the effectiveness of a kaupapa Māori 12-week exercise and lifestyle management programme on parameters of cardiac risk and quality of life. Methods: 12 Māori participants attended, 3 times per week over a 12-week period, for monitored, supervised, and individualised exercise. Participants performed a progressive aerobic-only programme for 6 weeks and then a combined aerobic and resistance training programme from weeks 7 through 12. Education sessions were chosen by participants. Results: There was a statistically significant improvement in waist circumference (–3.7 cm; p = .05), hip circumference (–4.6 cm; p = .03), systolic blood pressure (–22 mm Hg; p = .01), and HDL cholesterol (0.22 mmol/L; p = .01). In addition, physical (p = .05) and overall (p = .03) quality of life improved. Conclusion: A kaupapa Māori approach within a structured lifestyle management programme modifies cardiac risk parameters in Māori.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".