Patient-Oriented Research and Grounded Theory: A Case Study of How an Old Method Can Inform Cutting-Edge Research
Bibliographic record
Abstract
Creating evidence that is both scientifically rigorous and patient oriented in addressing patients’ needs is essential to informing health-care professionals’ practice and meeting patient needs. Patient-oriented research (POR) aims to address this 2-fold mandate by engaging and incorporating patients’ voices throughout the research process through a variety of techniques. Currently, there is little methodological rigor or guidance to help qualitative patient-oriented researchers design, collect, and analyze patient data. Classical grounded theory (GT) is arguably one of the most rigorous qualitative research methods, focusing on the development of theory from data grounded in participants’ voices. As such, classical GT is an ideal methodological approach for conducting POR due to its rigor, patient-oriented focus, and generation of an empirical model focused on the topic of interest. The purpose of this article is to describe the convergence and divergence between classical GT and POR, based on the current literature and pragmatically through an ongoing classical GT study focused on combat veterans’ perspective on Operational Stress Injuries (OSIs). By describing the methodological principles and their implementation in a POR study, we provide readers with both substantive and practical knowledge to utilize classical GT in POR studies, particularly within study populations that may be averse to or experience challenges in participating in research. Classical GT therefore provides patient-oriented researchers with a pragmatic methodological framework for engaging patients and generating rigorous evidence.
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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 | 0.112 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.037 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".