A realist evaluation of social care practitioners’ experiences with and understanding of applied healthcare research
Bibliographic record
Abstract
Abstract Social care practitioners are often under-represented in research activity and output. This article presents findings from a National Institute for Health and Care Research (NIHR) funded realist evaluation to understand and explain how, why, for whom, and in what contexts mental health social care practitioners engage with research. The study uses a current NIHR-funded study—REalist Synthesis Of non-pharmacologicaL interVEntions for antipsychotic-induced weight gain (RESOLVE)—as an illustrative example. Semi-structured interviews were undertaken with eighteen social care practitioners (SCPs) and data were analysed using a realist logic of analysis. Our refined programme theory describes SCPs’ current knowledge and interests in research, influenced by healthcare culture; their relationships with other healthcare professionals; protected time opportunities; and tailored invitations to hear their perspectives on healthcare needs of their clients. Underpinning the programme theory are seven context-mechanism-outcome configurations that propose evidence-informed contextually-sensitive causal explanations (i.e. mechanisms) that either facilitate or impede practitioners’ engagement with research. These findings highlight the need to provide tailored support to SCPs and build collaborative relationships with academics and other research-active health professionals. Better understanding of research engagement by SCPs will allow for evidence-based practice and better patient outcomes within these settings.
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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 | 0.362 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.005 | 0.007 |
| 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; 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".