Frameworks on self-management support for chronic disease: a multi-country qualitative study of the implementation process
Bibliographic record
Abstract
Introduction: Self-management support (SMS) is a comprehensive approach to chronic disease management where healthcare professionals and the wider healthcare system aim to enable people with chronic disease to manage their health and wellbeing. National and state-level health systems have produced SMS frameworks which propose changes at patient, provider, and organisational levels of the healthcare system. While the frameworks hold a lot of potential to reduce the burden of chronic disease, the process of implementing these frameworks is not yet understood.Methods: A grey literature search identified 8 SMS frameworks published between 2008 and 2018 within Australia, Canada, Ireland and the United Kingdom. Implementation leads of these frameworks were invited to participate as key informants in a qualitative study of framework implementation. Semi-structured telephone interviews were carried out with 6 key informants, representing 3 health systems. The Multiple Streams/Critical Juncture Approach to Implementation (1) was used as a theoretical lens in a thematic analysis of the interviews.Results: The process of implementation was influenced by policy streams affecting earlier policy stages including challenges around competing proactive versus reactive healthcare approaches; ambiguity in terminology of SMS; and an uncertain political environment where priorities and structures for implementation continued to change over time. Adapting plans in line with health service priorities and the continued input of patients and professionals, at the centre of SMS, were considered key to advancing implementation.Discussions: The findings suggest that policy stages are not discrete and that implementation is influenced by factors affecting agenda setting and policy formulation. Implementation of SMS was viewed as a long-term process. Implementation requires long-term mechanisms of communication across levels of the health system and deliverables within the health system to sustain support for the policy. Conclusions: SMS framework implementation can be advanced through an adaptive approach to SMS which is guided by the input of key stakeholders and is underpinned by long-term health system structures.Lessons learned: Similarities in the challenges and enablers across health systems suggests that learning across countries may help to advance the implementation of SMS frameworks though findings need to be considered within the context of each health system.Limitations: Three health systems are represented in this study and so this account of implementation may not apply to implementation of SMS frameworks in other countries.Suggestions for future research:Further research is needed to understand the perspective of people with chronic disease to identify the courses of action that should be prioritized to best meet their needs. Research can also explore the optimal structures which facilitate long-term communication across levels of the health system. 1. Howlett M. Moving policy implementation theory forward: A multiple streams/critical juncture approach. Public Policy and Administration. 2019;34(4):405-430.
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 | 0.038 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".