Developing a Comprehensive Search Strategy for Evidence Based Systematic Reviews
Bibliographic record
Abstract
Objective: As the health care field moves towards evidence-based practice, it becomes ever more critical to conduct systematic reviews of research literature for guiding programmatic activities, policy-making decisions, and future research. Conducting systematic reviews requires a comprehensive search of behavioral, social, and policy research to identify relevant literature. As a result, the validity of the systematic review findings and recommendations is partly a function of the quality of the systematic search of the literature. Therefore, a carefully thought out and organized plan for developing and testing a comprehensive search strategy should be followed. Methods: The comprehensive search strategies, including automated and manual search techniques, were developed, tested, and implemented to locate published and unpublished citations to build a database of HIV/AIDS and STD literature for the CDC’s HIV Prevention Research Synthesis Project. The search incorporates various automated and manual search methods to decrease the chance of missing pertinent information. The automated search was implemented in MEDLINE, EMBASE, PsycINFO, Sociological Abstracts and AIDSLINE some of the key databases for biomedical, psychological, behavioral science, and public health literature. These searches utilized indexing, keywords including truncation, proximity, and phrases. The manual search method includes physically examining journals (hand searching), reference list checks, and researching key authors. Results: Using automated and manual search components, the PRS search strategy retrieved 17,493 HIV/AIDS/STD prevention focused articles for the years 1988-2005. The automated search found 91% and the manual search contributed 9% of the articles reporting on HIV/AIDS or STD interventions with behavior/biologic outcomes. Among the automated search citations, 48% were found in one database only (20% MEDLINE, 18% PsycINFO, 8 % EMBASE, 2% Sociological Abstracts). Conclusions: A comprehensive base of literature requires searching multiple databases and methods of manual searching in order to locate all relevant citations. Understanding the project needs, the limitations of different electronic databases, and other methods for developing and refining a search are vital in planning an effective and comprehensive search strategy. Reporting standards for literature searches as part of the broader push for procedurally transparent and reproducible systematic reviews is not only advisable, but good evidence-based practice.
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 | 0.213 | 0.404 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.014 |
| Bibliometrics | 0.096 | 0.060 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 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, 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".