An Analysis of Literature Reviews in the Context of Healthcare Program Assessment
Bibliographic record
Abstract
When conducting an assessment of existing literature, various types of literature reviews can be utilized. More specifically, each type has its advantages, disadvantages, and ideal circumstances in which it should be used. This paper explores the systematic review, scoping review, and rapid review in the context of research that seeks to assess existing health care programs. Evidence suggests that the systematic review is the most rigorous and in-depth, but often takes a significant amount of time to complete. The scoping review is less rigorous and used to identify what is known about a specific topic in the literature. The rapid review is similar in rigour to the systematic review, but takes less time and is often used in situations where data needs to be obtained quickly. In this paper, strengths and weaknesses, alongside examples of each review are given. They are then analyzed to see which would be best to utilize for the topic of assessing existing health care programs. In closing, it is decided that the rapid review is the best method due to its limited time frame and extensive rigour, which is the most beneficial when assessing health care programs.
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 | 0.102 | 0.346 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.078 | 0.097 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".