Difference between Quantitative and Qualitative Research Question- PICO vs. SPIDER
Bibliographic record
Abstract
Purpose: The research question, an empirical component of research, is used for conceptualization, methodology selection, and patient recruitment when aiming to answer a complex phenomenon.PICO (patient, intervention, comparison, and outcome) is a commonly employed/used framework for formulating a research question in quantitative studies.The PICO framework does not capture all the components of a qualitative research question thus, PICO may not be a suitable framework.To describe difference between qualitative and quantitative research questions and what are the main components of these questions.Methodology: Non-systematic review of qualitative and quantitative studies exploring expectations in preoperative sciatica and or chronic low back pain patients.We compared the research question between qualitative and quantitative studies, using SPIDER and PICO framework.Findings: We reviewed five qualitative studies, and six quantitative studies that explored expectation in sciatica or chronic low back pain patients undergoing surgical or nonsurgical interventions.Qualitative studies differed from quantitative studies as the former do not test hypotheses, but instead generated them.Qualitative studies are used to explain complex processes such as patients' perceptions, experiences, attitudes, and opinions.The PICO framework did not capture all the components of a qualitative research question thus, SPIDER should be preferred over the PICO framework.Discussion: Understanding the difference in qualitative and quantitative research questions will be of particular importance to new researchers and students planning to conduct qualitative research.
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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 | 0.455 | 0.667 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".