A critical realist approach to understanding and evaluating heart health programmes
Bibliographic record
Abstract
Secondary prevention programmes for Coronary Heart Disease (CHD) aim to reduce cardiovascular risks and promote health in people with heart disease. Though programmes have been associated with health improvements in study populations, access to programmes remains low, and quality and effectiveness is highly variable. Current guidelines propose significant modifications to programmes, but existing research provides little insight into why programme effectiveness varies so much. Drawing on a critical realist approach, this article argues that current research has been based on an impoverished ontology, which has elements of positivism, does not explore the social determinants of health or the effects on outcomes of salient contextual factors, and thereby fails to account for programme variations. Alternative constructivist approaches are also weak and lacking in clinical credibility. An alternative critical realist approach is proposed that draws on the merits of subjectivist and objectivist approaches but also reflects the complex interplay between individual, programme-related, socio-cultural and organizational factors that influence health outcomes in open systems. This approach embraces measurement of objective effectiveness but also examines the mechanisms, organizational and contextual-related factors causing these outcomes. Finally, a practical example of how a critical realist approach can guide research into secondary prevention programmes is provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.108 | 0.124 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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, 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".