Understanding the sustainment of population health programmes from a whole-of-system approach
Bibliographic record
Abstract
Abstract Background Population health prevention programmes are needed to reduce the prevalence of chronic diseases. Nevertheless, sustaining programmes at a population level is challenging. Population health is highly influenced by social, economic and political environments and is vulnerable to these system-level changes. The aim of this research was to examine the factors and mechanisms contributing to the sustainment of population prevention programmes taking a systems thinking approach. Methods We conducted a qualitative study through interviews with population health experts working within Australian government and non-government agencies experienced in sustaining public health programs at the local, state or national level (n = 13). We used a deductive thematic approach, grounded in systems thinking to analyse data. Results We identified four key barriers affecting program sustainment: 1) short term political and funding cycles; 2) competing interests; 3) silo thinking within health service delivery; and 4) the fit of a program to population needs. To overcome these barriers various approaches have centred on the importance of long-range planning and resourcing, flexible program design and management, leadership and partnerships, evidence generation, and system support structures. Conclusion This study provides key insights for overcoming challenges to the sustainment of population health programmes amidst complex system-wide changes.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".