Review of Mixed-Methods Research in Nursing
Bibliographic record
Abstract
BACKGROUND: Inadequate justification for using mixed-methods and inadequate data integration compromises the rigor of mixed-methods studies, and data integration remains a challenge for nurse researchers. OBJECTIVES: The aim of the study was to determine the 5-year prevalence of mixed-methods research in nursing journals and to determine the extent of integration of qualitative and quantitative findings. METHODS: Ten journals were hand-searched, and additional search was conducted within three databases. Prevalence was calculated by counting the number of published mixed-methods studies divided by the number of published studies over 5 years. Three reviewers independently performed methodological assessment using a checklist based on guidelines by expert methodologists. RESULTS: Prevalence of mixed-methods studies was 1.89%. Concerning methodological assessment, of 175 studies, 29% did not provide an explicit label of the study design and four studies incorrectly labeled the design. In total, 31% of the studies did not justify using mixed methods, 95% did not identify the research paradigm, and 78% did not state the weight given to individual phases. The extent of data integration was 73%, but 83% of studies integrated data using narrative summaries with integration occurring at the interpretation (69.8%). Few studies used joint displays (10.9%), transformation (3.1%), and triangulation (1.6%) for data integration. DISCUSSION: Mixed-methods research is still in its infancy in nursing, and researchers encounter challenges during its conduct, analysis, and reporting. There is a need to determine researchers' attitudes and challenges toward using mixed methods and educate them about advanced mixed methods. Emphasis should be placed on use of advanced data integration methods so that the rigor and quality of mixed research can be enhanced in nursing research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.198 | 0.449 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.034 | 0.028 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".