Grey Literature Searching for Health Sciences Systematic Reviews: A Prospective Study of Time Spent and Resources Utilized
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
OBJECTIVE: To identify estimates of time taken to search grey literature in support of health sciences systematic reviews and to identify searcher or systematic review characteristics that may impact resource selection or time spent searching. METHODS: A survey was electronically distributed to searchers embarking on a new systematic review. Characteristics of the searcher and systematic review were collected along with time spent searching and what resources were searched. Time and resources were tabulated and resources were categorized as grey or non-grey. Data was analyzed using Kruskal-Wallis tests. RESULTS: Out of 81 original respondents, 21% followed through with completion of the surveys in their entirety. The median time spent searching all resources was 471 minutes, and of those a median of 85 minutes were spent searching grey literature. The median number of resources used in a systematic review search was four and the median number of grey literature sources searched was two. The amount of time spent searching was influenced by whether the systematic review was grant funded. Additionally, the number of resources searched was impacted by institution type and whether systematic review training was received. CONCLUSIONS: This study characterized the amount of time for conducting systematic review searches including searching the grey literature, in addition to the number and types of resources used. This may aid searchers in planning their time, along with providing benchmark information for future studies. This paper contributes by quantifying current grey literature search patterns and associating them with searcher and review characteristics. Further discussion and research into the search approach for grey literature in support of systematic reviews is encouraged.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.139 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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