Search strategy for a study on “ Mobile Applications and Digital Platforms that support Type 2 Diabetes Self-Management and Care for Immigrants and Refugees: A Scoping Review”
Bibliographic record
Abstract
A systematic search was conducted in [March 2025] across six electronic databases: PubMed, MEDLINE (via OVID), Embase (via OVID), Scopus, CINAHL (via EBSCO), and the Cochrane Library (via OVID). The search strategy was developed in collaboration with a medical librarian at Dalhousie University. The search included three primary concepts: (1) type 2 diabetes, (2) immigrant and refugee populations, and (3) digital health interventions with multilingual and/or culturally tailored components. Free-text keywords and controlled vocabulary terms (e.g., MeSH headings for MEDLINE and PubMed) were used in combination.. Literature published in English between January 1, 2011, and March 1, 2025, was included. This time frame was selected due to the increasing development and uptake of smartphone applications starting in the early 2010s [53]. Reference lists of relevant systematic and scoping reviews were screened to identify additional studies not captured in the database searches. Experts in the field of immigrant and refugee health were also contacted to identify additional relevant articles or gray literature. All identified records were imported into Covidence software for deduplication and screening.
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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.010 |
| Bibliometrics | 0.043 | 0.037 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.094 | 0.009 |
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".