Advancing the methodology of mapping reviews: A scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This scoping review aims to identify and systematically review published mapping reviews to assess their commonality and heterogeneity and determine whether additional efforts should be made to standardise methodology and reporting. The following databases were searched; Ovid MEDLINE, Embase, CINAHL, PsycINFO, Campbell collaboration database, Social Science Abstracts, Library and Information Science Abstracts (LISA). Following a pilot-test on a random sample of 20 citations included within title and abstracts, two team members independently completed all screening. Ten articles were piloted at full-text screening, and then each citation was reviewed independently by two team members. Discrepancies at both stages were resolved through discussion. Following a pilot-test on a random sample of five relevant full-text articles, one team member abstracted all the relevant data. Uncertainties in the data abstraction were resolved by another team member. A total of 335 articles were eligible for this scoping review and subsequently included. There was an increasing growth in the number of published mapping reviews over the years from 5 in 2010 to 73 in 2021. Moreover, there was a significant variability in reporting the included mapping reviews including their research question, priori protocol, methodology, data synthesis and reporting. This work has further highlighted the gaps in evidence synthesis methodologies. Further guidance developed by evidence synthesis organisations, such as JBI and Campbell, has the potential to clarify challenges experienced by researchers, given the magnitude of mapping reviews published every year.
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.
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 | 0.348 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it