Data Analysis and Presentation in Integrative Reviews: A Narrative Review
Bibliographic record
Abstract
Integrative reviews are invaluable for synthesizing literature to guide practice. This review examined the nature and range of data analysis approaches and the methods used for data presentation in integrative reviews. We reviewed 151 integrative reviews published from January to December 2019 in nursing journals listed in Journal Citation Report 2020. Summary tables were used for data extraction and researcher-developed questions-based process for synthesis. Data analysis and synthesis methods were categorized as non-specific, inductive, deductive, and framework-based. Majority of reviews did not explicitly delineate data analysis and synthesis methods (n = 67) or used inductive methods (n = 55). Limited reviews used deductive (n = 13) and framework-based methods (n = 13). Most of the reviews used narrative descriptions for presentations of findings, but some reviews also used innovative tables, concept maps, frameworks, and word clouds to enhance data presentation. The findings provide a comprehensive overview of the diversity of methods for data analysis and presentation in integrative reviews.
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
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 | 0.207 | 0.460 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.037 | 0.035 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".