Pragmatism as a paradigm for patient‐oriented research
Bibliographic record
Abstract
BACKGROUND: Mixed methods research studies continue to pervade the field of health care, where pragmatism as a research paradigm and patient-oriented research (POR) as an engagement strategy are combined to strengthen the process and outcomes of the research. Pragmatists use the most appropriate research methods to address issues at hand, where complex social problems need multipronged approaches. As an emerging healthcare research strategy, POR actively engages individuals with lived experience across all stages of the research process. While POR continues to garner attention within mixed-methods research designs, there is a paucity of literature that considers POR in relation to pragmatism. OBJECTIVE: As POR grows in popularity within the field of health care, there is a need to explore the theoretical and epistemological alignment with pragmatism and the implications to research. METHODS: To address this need, we provide a critical review of the literature to examine the synergies between POR and pragmatism, and argue for the adoption of pragmatism as a paradigm for conducting POR. MAIN RESULTS: This article begins with a discussion of the philosophical underpinnings informing the pragmatic paradigm. It then identifies key alignments between POR and pragmatism across three intersecting concepts: democratic values, collaborative approaches to problem-solving and the pursuit of social justice. DISCUSSION AND CONCLUSIONS: Reflecting on our experiences engaging with patient partners in a mixed-methods POR study titled READY2Exit, we illustrate the relevance of pragmatism to POR by applying these concepts to practice. Implications and considerations for conducting POR within the pragmatic paradigm are also described. PATIENT OR PUBLIC CONTRIBUTION: This paper provides a critical review of the literature and did not directly involve patients or the public. The authors reflected on their experiences collaborating with five young adult patient partners in the READY2Exit study (case exemplar described in this article) to demonstrate the relevance of the pragmatic paradigm to POR. We acknowledge and thank the young adult patient partners for their contributions to the research, for encouraging us to think critically about patient engagement in research, and for sharing their experiences.
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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 | 0.378 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.012 | 0.176 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.009 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".