Qualitative dyadic analysis in care partnership research: a scoping review
Bibliographic record
Abstract
BACKGROUND: Chronic illness impacts not only individuals affected by it, but also those who care for them. Care partnerships recognize that health conditions are often shared, dyadic experiences. Qualitative dyadic analysis, which foregrounds the dyad as the unit of analysis, is a method that can enhance understanding of illness as a joint experience. However, when perspectives of dyad members are collected separately, their subsequent analysis as a unit can be challenging. OBJECTIVE: To review and summarize qualitative literature where data have been collected through separate individual interviews with patient and care partner dyads and analyzed at the dyadic level. METHODS: A scoping review guided by Joanna Briggs Institute methodology was undertaken. Databases (Ovid's Medline, Embase, and PsycINFO; EBSCO CINAHL; and ProQuest Sociological Abstracts) were searched in February 2024. Eligible articles included peer-reviewed literature published in English from 2010 onwards documenting qualitative dyadic analysis of individual interviews collected from patient and care partner dyads. Title and abstracts were screened and the full text of all potentially eligible articles was reviewed by two independent reviewers. Data were extracted using a table and results were summarized using frequency counts and qualitative content analysis. RESULTS: 7,494 records were identified and screened. 113 reports of 112 unique studies fulfilled eligibility criteria and were included. Numerous methodologies and analytic methods were reported, many of which incorporated methods from different qualitative traditions, often with variable sequencing of analytic steps that were infrequently well described. Studies were not routinely conceptualized at the dyadic level and underlying epistemological assumptions were rarely discussed despite their essential role in grounding dyadic analysis. CONCLUSIONS: When conducting qualitative dyadic analysis, researchers should consider dyadic study conceptualization from study outset. The purpose of the analysis, the analytic steps taken, and their alignment with underlying epistemology and other incorporated methodologies should be clearly documented and reported.
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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 | 0.105 | 0.240 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.044 | 0.050 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| 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, 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".