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Record W2960067330 · doi:10.1097/nnr.0000000000000372

Review of Mixed-Methods Research in Nursing

2019· review· en· W2960067330 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNursing Research · 2019
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNursing researchPsychologyNursingMedicine

Abstract

fetched live from OpenAlex

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.

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.174
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1740.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0050.012
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.010
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.963
GPT teacher head0.894
Teacher spread0.069 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it