Qualitative Methods in Systemic Sclerosis Research
Bibliographic record
Abstract
Qualitative research methods are important tools that are frequently underused in medical research, particularly in systemic sclerosis (scleroderma, SSc) research. In this issue of The Journal , Nakayama, et al report a thematic synthesis of qualitative studies of patients’ perspectives and experiences living with SSc1. This editorial serves as a brief introduction to qualitative research, addressing the questions: What is it? How is it used? How is it different from quantitative research? What are key indicators of its rigor? Quantitative research is a form of study design that uses statistical methods or other means of quantification to address a research question. It involves deductive approaches and hypothesis testing to approximate the truth and handling of uncertainty2. Quantitative methods generally evaluate participants in settings removed from the natural environment (e.g., randomized controlled trials) and attempts are made to control for confounding factors. In contrast, qualitative research is a form of study design that uses systematic and reproducible methods to explore experiences, behaviors, and beliefs about a concept or phenomenon. Qualitative research commonly involves interpretative forms of analysis in which the unit of study is often a participant’s experienced reality3. Results are a representation of reality rather than an approximation of the truth (see Table 1). View this table: Table 1. Comparison of quantitative and qualitative methods. Qualitative methods may be preferable if one wishes to develop a new theory, explore an unknown phenomenon, evaluate the meaning of a concept, or understand a phenomenon. Qualitative research can provide more detailed descriptions and nuanced understanding of a concept, in contrast to a “cause and effect” relationship. A qualitative approach may facilitate a more in-depth understanding of quantitative results, answering “what … Address correspondence to Dr. S.R. Johnson, Division of Rheumatology, Ground Floor, East Wing, Toronto Western Hospital, 399 Bathurst St., Toronto, Ontario M5T 2S8, Canada. E-mail: Sindhu.Johnson{at}uhn.on.ca
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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 | 0.219 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".