Information access and systematic reviews: A discussion
Bibliographic record
Abstract
Systematic reviews are prevalent across various healthcare disciplines, yet they remain one of the least engaged methodologies in nursing. Increasingly, massive amounts of healthcare studies and information are published in various mediums, which may made it difficult for nurses to keep up with primary research evidence. The purpose of this discussion paper is to present a methodology that can be used to search for relevant materials, sort through large volumes of information, and make decisions regarding possible study selection for review. The intention of this paper is to describe the process involved in mapping out what is known from the existing literature about a specific area of interest, as well as the strategies used to delimit the number and type of materials to be included in a systematic review. An overview of the process of identifying relevant materials to include in a review is presented. Specifically, determining inclusion and exclusion criteria, search strategies, and selecting studies for inclusion in a systematic review are discussed. A case study of an existing systematic review that evaluated interventions for reducing the number of hospital readmissions following heart failure is used to guide this discussion.
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 | 0.421 | 0.567 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.031 | 0.037 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.038 | 0.085 |
| Open science | 0.010 | 0.027 |
| Research integrity | 0.046 | 0.029 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".