Do Research–Practice Partnerships Offer a Promising Approach to Producing Research that Improves Social Care Practice and Outcomes?
Bibliographic record
Abstract
There are many pressing questions about how to deliver adult social care services. Where research evidence exists to address these questions, there is often limited use by social care commissioners, providers and the workforce. Sometimes this is attributed to the lack of perceived relevance and accessibility of the research itself, at other times it is considered to be a matter of individual and organizational capacity. As things stand, there is a gap between social care research and practice. Improving interaction between different stakeholders in the research process is a contemporary mechanism for promoting the production of research that is useful, usable and used. This paper describes one collaborative approach called Research Practice Partnerships (RPPs). These partnerships share the goal of benefit for all partners and are supported by a growing international evidence base. This paper summarizes some of the key literature from different countries and contexts where the approach has been tried. It highlights the main features of RPPs, introduces a project setting up three new partnerships in the care home sector in England and highlights aspects of the theory of change that will guide the evaluation of the partnerships. In doing so, the paper introduces a promising collaborative approach to a social care audience and considers whether RPPs have the potential to achieve meaningful and impactful research in social care contexts.
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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 | 0.329 | 0.298 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.046 | 0.073 |
| Open science | 0.007 | 0.046 |
| Research integrity | 0.024 | 0.015 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".