Qualitative research in evidence‐based practice: a valuable partnership
Bibliographic record
Abstract
Purpose The purpose of this paper is to discuss the nature of the qualitative research paradigm, with a particular emphasis on the marginalization of qualitative approaches within the current discourse of evidence‐based librarianship. Design/methodology/approach The paper presents examples of qualitative research in the field of library and information studies, reviews the discourse of EBL as it relates to qualitative research, and also draws on debates in the health sciences on the role of qualitative research in evidence‐based practice. Findings EBL levels of evidence must evolve to include qualitative research, as these methods best suit many of the research questions addressed in LIS contexts. Originality/value There is currently little acknowledgement of the value of qualitative research for EBL; this paper dispels this notion, and calls for EBL to embrace these methods.
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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 | 0.400 | 0.387 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.030 | 0.027 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".