Evaluation of an integrated care model using the RE-AIM \nframework: A case study of a community-based lifestyle \nservice in rural Lincolnshire, UK
Bibliographic record
Abstract
Introduction: Many unhealthy behaviours such as tobacco smoking, poor diet, harmful alcohol use, and physical inactivity tend to group. In England, around a quarter of people are engaged in three or more unhealthy behaviours, contributing to a higher risk of ill health. Interventions, known as integrated lifestyle services (ILS), encourage sustained health changes and reduced costs. There is limited evidence on the effectiveness of ILSs in rural settings and factors that impact implementation. One You Lincolnshire is a non-NHS provider working with GP practices, community care services and local charities to offer online, digital lifestyle support for individuals with long-term health conditions. \n \nMethods: This study aimed to identify the impact of addressing unhealthy behaviours for an individual through One You Lincolnshire (OYL), establish how OYL has been implemented, and highlight any potential risks and challenges that may impact the intervention in the future. This presentation will give an overview of the key findings from phase 1 of the evaluation, which used a mixed-method approach and was co-produced with a multi-stakeholder group. The study had a total of 53 participants, including Service Users (n = 24), Health Professionals (n= 9), One You Lincolnshire staff (n=17) and Stakeholders (n=3). \n \nKey Findings: Thematic analysis was used to identify key themes in service delivery and implementation. From the interviews and focus groups, the key findings were as follows: \n \n- Online delivery model offered much greater accessibility for a wide range of clients in rural areas. \n-Once referred, an integrated service model decreased barriers for stigmatised health needs such as smoking cessation or alcohol reduction. \n-A legacy of decommissioning services led to apprehension for some health professionals to adopt the model. \n \nThe results from phase 1 highlight that digital service delivery during the covid pandemic may increase accessibility for individuals with long-term health conditions. Also, participating in multiple pathways suggest an increase in sustained long-term changes. \n \n Conclusion: Integrated lifestyle services could be an effective model to tackle co-morbidities with opportunities to work with community partners to develop robust care pathways. However, there are still challenges in adopting the model by GP practices and the need to further explore the service's health outcomes and cost-effectiveness. \n \n Implications: These findings will be used to make real-time changes to One You Lincolnshire service delivery and contribute to a broader body of research on the implementation of ILS in rural settings. One limitation of the study was the dropout of some participants between survey and interview, resulting in fewer service users' perspectives than desired. However, phase 2 will focus on a more extensive dataset to triangulate the findings.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| 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".