Qualitative case study methodology in nursing research: an integrative review
Bibliographic record
Abstract
AIM: This paper is a report of an integrative review conducted to critically analyse the contemporary use of qualitative case study methodology in nursing research. BACKGROUND: Increasing complexity in health care and increasing use of case study in nursing research support the need for current examination of this methodology. DATA SOURCES: In 2007, a search for case study research (published 2005-2007) indexed in the CINAHL, MEDLINE, EMBASE, PsychINFO, Sociological Abstracts and SCOPUS databases was conducted. A sample of 42 case study research papers met the inclusion criteria. METHODS: Whittemore and Knafl's integrative review method guided the analysis. RESULTS: Confusion exists about the name, nature and use of case study. This methodology, including terminology and concepts, is often invisible in qualitative study titles and abstracts. Case study is an exclusive methodology and an adjunct to exploring particular aspects of phenomena under investigation in larger or mixed-methods studies. A high quality of case study exists in nursing research. CONCLUSION: Judicious selection and diligent application of literature review methods promote the development of nursing science. Case study is becoming entrenched in the nursing research lexicon as a well-accepted methodology for studying phenomena in health and social care, and its growing use warrants continued appraisal to promote nursing knowledge development. Attention to all case study elements, process and publication is important in promoting authenticity, methodological quality and visibility.
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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 | 0.059 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".