Search strategy for scientific and grey literature
Bibliographic record
Abstract
The search strategy will aim to locate both published and unpublished sources from the scientific and grey literature as well as relevant websites and reports. For the scientific literature, an initial search of Medline (Ovid) was undertaken to identify articles on the topic. The text words contained in the titles and abstracts of relevant articles, and the index terms used to describe the articles were used to develop a full search strategy for four electronic databases and information sources. Databases to be searched include Medline (Ovid), Web Of Science, Cinhal and Embase. The search was conducted in August 2022 with the three following concepts: 1) Adults with physical and/or sensory disability, 2) Digital platform, and 3) Sport or physical activity. The search strategy collaboratively developed by the research team based on the PICO framework (Richardson et al., 1995), including all identified keywords and index terms, will be adapted by the librarian for each included database and/or information source. The reference list of all included sources of evidence will be screened for additional studies. For the grey literature, a search will be performed in Advanced Search - Google search engine. Google search requires creating several search strategies containing multiple combinations of search terms. Thirty-two unique search strategies were applied to Advanced Search - Google. Each search was repeated using a VPN with IP addresses based in the following countries: Canada, USA, France, Belgium, UK, and Australia. A comprehensive Internet search is impossible; thus, relying on the power of relevancy ranking within Google search engine, only the first five pages of each search’s hits (i.e., 50 results) will be reviewed, using the title and short descriptive text underneath. This number of pages was chosen to capture many of the most relevant hits while providing a volume feasible for screening. Potentially relevant records will be added to a file in Zotero© shared by the research team and then each source will be entered into an Excel spreadsheet for screening purposes.
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 | 0.071 | 0.163 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.008 |
| Bibliometrics | 0.085 | 0.046 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.191 | 0.030 |
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".