The Development of PRITEM Reporting Guideline for Mapping Reviews
Bibliographic record
Abstract
Mapping reviews and Evidence Gap Maps (MR/EGM) have gained significant attention as a method for synthesizing evidence. These products aim to identify areas where evidence is adequate and where gaps exist, guiding decision-making and setting future research priorities. However, there are notable differences in terminology, reporting formats, and content across various fields, organizations, and authors. The PRITEM (Preferred Reporting Items for Mapping Reviews) project aims to address these discrepancies and promote standardized reporting. The PRITEM is being developed collaboratively by researchers from institutions including Lanzhou University, China; University of Ottawa, Canada; Campbell Collaboration; JBI; Africa Centre for Evidence (ACE), South Africa; Global Development Network; and Newcastle University, UK. The PRITEM project will adhere to the 'Guidance for developers of health research reporting guidelines' in developing its reporting guideline. A multi-stage approach will be adopted, which includes identifying the need of the checklist, obtaining funding and registering the protocol, establishing PRITEM working groups, reviewing the literature, conducting a Delphi process, holding a consensus meeting, and disseminating the findings. We will establish a multidisciplinary international team of experts to develop the guideline. Based on the results of scoping reviews of relevant literature, we will conduct surveys with international experts and reach a consensus to determine the final checklist. The PRITEM guidance on mapping reviews will serve as a valuable resource for developers of mapping reviews, thus enhancing the overall reporting quality. It will better promote the implementation of available evidence and guide future research priorities, ultimately reducing the waste of research resources.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads 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".