Prioritizing qualitative meta-synthesis findings in a mixed methods systematic review study: A description of the method
Bibliographic record
Abstract
AIM(S): To describe a sequential mixed methods review method that prioritized synthesized qualitative evidence from primary studies to explain the complexities of older persons with multiple chronic conditions' unplanned readmission experiences. BACKGROUND: Segregated mixed methods review studies frequently prioritize quantitative evidence synthesis to examine the effectiveness of interventions; utilizing qualitative evidence to explain quantitative data. There is a lack of guidance about how to prioritize qualitative evidence. RESULTS: Five procedural steps were developed to prioritize qualitative evidence synthesis. In Step 1, research questions were developed. In Step 2, databases were searched, studies were mapped to their method (qualitative or quantitative) and appraised. In Step 3, meta-synthesis and applied thematic analysis were used to synthesize extracted qualitative evidence about the psychosocial processes and factors that influenced unplanned readmission. In Step 4, quantitative evidence was synthesized using vote counting to determine the factors influencing unplanned readmission. In Step 5, a matrix was used to compare, determine the agreement between the qualitative and quantitative evidence, juxtapose findings, and uphold validity. Factors were mapped to the model of psychosocial processes and analytic themes. CONCLUSION: Prioritizing qualitative evidence synthesis in a mixed methods review study prioritizes participants' experiences, perspectives, and voices to understand complex clinical problems from participants who experienced the event. Synthesizing and integrating evidence facilitates the construction of holistic new understandings about phenomenon and expands mixed methods systematic review methods. IMPLICATIONS: Prioritizing patients' perspectives is useful for developing new client-centered interventions, establishing best practices for future reviews, generating theories, and expanding research methods.
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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 | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Systematic review | 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 | 0.444 | 0.508 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".