Critically Appraising Qualitative Research: a Guide for Clinicians More Familiar With Quantitative Techniques
Bibliographic record
Abstract
OBJECTIVES: Papers using qualitative methods are increasingly common in psychiatric journals. This overview is an introduction to critically appraising a qualitative paper for clinicians who are more familiar with quantitative methods. CONCLUSIONS: Qualitative research uses data from interviews (semi-structured or unstructured), focus groups, observations or written materials. Data analysis is inductive, allowing meaning to emerge from the data, rather than the more deductive, hypothesis centred approach of quantitative research. This overview compares and contrasts quantitative and qualitative research methods. Quantitative concepts such as reliability, validity, statistical power, bias and generalisability have qualitative equivalents. These include triangulation, trustworthiness, saturation, reflexivity and applicability. Reflexivity also shares features of transference. Qualitative approaches include: ethnography, action-assessment, grounded theory, case studies and mixed methods. Qualitative research can complement quantitative approaches. An understanding of both is useful in critically appraising the psychiatric literature.
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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 | 0.222 | 0.353 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".