Realist evaluations: relevance to pharmacy practice and education
Bibliographic record
Abstract
OBJECTIVES: To explore realist evaluations, focusing on their utility in pharmacy-related practice and education research. METHODS: Realist evaluations are a theory-driven approach to research that provides a robust account of the nature of programs and how they work in answering the question, "what works (or does not work) for whom in what circumstances and why (not)?". In realist evaluations, the context in which it is implemented, the mechanisms that trigger change, and the outcomes that result are explored. KEY FINDINGS: Realist evaluations provide a more informed approach to targeting and refining programs to suit a diverse and complex healthcare system. Realist evaluations have been used by researchers from a variety of health professional disciplines to evaluate programs and interventions to improve public health, health care policy, and tertiary education institutions. Examples of the complex settings where researchers have used this method for the evaluation process include supervision training workshops, rural immersion training programs, faculty development courses, safe medication administration programs, and the ability of managers to effect change. CONCLUSION: Realist evaluations are powerful methodological approaches for studying complex interventions and support managing the nuances associated with constrained resources, differing governing policies and diverse population groups.
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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 | 0.265 | 0.551 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".