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Record W2985333871 · doi:10.1111/jan.14264

Characteristics of joint displays illustrating data integration in mixed‐methods nursing studies

2019· review· en· W2985333871 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

VenueJournal of Advanced Nursing · 2019
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNursingJoint (building)PsychologyMedicineEngineering

Abstract

fetched live from OpenAlex

AIMS: To identify the characteristics of joint displays illustrating the data integration in mixed-methods nursing studies and to make recommendations for effective use of joint displays for the integration of qualitative and quantitative data in mixed-methods studies. DESIGN: Discussion Paper. DATA SOURCES: We have completed this paper as a part of a mixed-methods prevalence review of 190 studies published in nursing journals. We searched 10 nursing journals and three databases from January 2014-April 2018, additional journal search was performed from May-September 2018. We reviewed 17 studies that used joint displays as the method of data integration. Using a joint display typology, checklists, summary tables, and personal experiences of using joint displays, we evaluated the quality of displays. IMPLICATIONS FOR NURSING: Nurse researchers should use advanced data integration approaches to increase the rigour of the mixed-methods studies. Joint displays can enable nurse researchers to efficiently integrate and synthesize the qualitative and quantitative data in mixed-methods studies. However, nurse researchers should clearly label the type and title of the display, include both qualitative and quantitative data and interpretations, and highlight the mixed-methods interpretations as confirmed, divergent, or expanded in the displays. CONCLUSION: Joint displays are adopted for data integration in nursing mixed-methods studies. Improvements are required concerning data presentation in the displays. Researchers should provide clear titles and supporting data and inferences and identify the meta-inferences by assessing the fit between quantitative and qualitative data. IMPACT: Despite the importance of integration in mixed methods, reviews indicated a consistent lack of integration in nursing research. Joint displays are structured frameworks used for the integration and synthesis of the qualitative and quantitative data at the analysis and interpretation levels. The discussed typology and characteristics of displays can enable nurse researchers to enhance the quality and presentation of integrated results in mixed-methods studies.

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.

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)0.010
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.854
GPT teacher head0.775
Teacher spread0.079 · 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