Understanding how self-management interventions work for disadvantaged populations living with chronic conditions: protocol for a realist synthesis
Bibliographic record
Abstract
INTRODUCTION: Self-management programmes are complex interventions aimed at improving the way individuals self-manage chronic conditions, but there are questions about the overall impact of these programmes on disadvantaged populations, in terms of their capacity to engage with and receive the benefits from these initiatives. Given the increased resources being directed towards self-management initiatives, clinicians and policy makers need knowledge on how self-management interventions work for these populations. Most systematic reviews of self-management interventions do not consider the complex interactions between implementation contexts, intervention strategies, and mechanisms that influence how self-management interventions work in real life for disadvantaged groups. METHODS: To address the need for better understanding of these mechanisms and to create context-relevant knowledge, we are conducting a realist synthesis of evidence on self-management interventions for disadvantaged populations living with chronic conditions. The primary research question is: What are the key mechanisms operating in chronic condition self-management interventions among disadvantaged populations? In this protocol, we outline the steps we will take to identify the programme theory for self-management interventions and candidate middle-range theories; to search for evidence in academic and grey literature; to appraise and extract the collected evidence; to synthesise and interpret the findings to generate key context-mechanism-outcome configurations and to disseminate results to relevant stakeholder and to peer-review publications. DISSEMINATION: Understandings of how chronic conditions self-management interventions work among disadvantaged populations is essential knowledge for clinicians and other decision makers who need to know which programmes they should implement for which groups. Results will also benefit medical researchers who want to direct effort towards current gaps in knowledge in order to advance the self-management field. In addition, the study will make a contribution to the evolving body of knowledge on the realist synthesis method and, in particular, to its application to behaviour change interventions for disadvantaged populations.
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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 | 0.118 | 0.191 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.135 | 0.023 |
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".