Knowledge Synthesis in Health, Wellness and Social Care Research: The Fundamentals of Conducting Comprehensive Reviews
Bibliographic record
Abstract
Knowledge synthesis is often a term that is widely used to define the process of summarizing and integrating research findings into the existing field of research of a specific topic. While knowledge syntheses can take many forms, it is commonly produced as a review of previously published literature in a specific field. With the recent tremendous increase in scientific, especially health, publications, conducting literature reviews has become an absolute necessity for investigators to scope out the body of research work that has already been done. Literature reviews provide a unique function of providing a clear and articulate understanding of the extent of previous work that has been done such that resources are not wasted in redundant duplication. Moreover, literature reviews can serve multiple purposes such as providing context to current crises, efficiently summarizing previously published work, identifying gaps in the literature of a specific topic, and aiding the overall advancement of knowledge in the research field of interest. In this manuscript, we provide detailed general steps for conducting a review based on standard and common methodological frameworks used to inform and conduct knowledge syntheses.
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 | 0.470 | 0.624 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".