Application of the Life Grid in Oral Health Research: A Scoping Review.
Bibliographic record
Abstract
AIM: The life grid is a retrospective interview-based tool used to enhance recall of past events. This scoping review examines the use of the life grid in oral health research and its applications. METHODS: Using the Joanna Briggs Institute framework, Medline (Ovid), CINAHL, PsycInfo, ERIC, MedEdPortal, Web of Science Core Collection, ProQuest, and Google Scholar were searched with "life grid" and "oral" as initial keywords. Then, two reviewers screened the records independently. Studies published until April 21, 2022, were added, regardless of language. Data were summarised narratively and in a comprehensive table focused on seven main areas. RESULTS: A total of 22 studies were included from 724 initially identified records. The life grid was used in different forms, in various qualitative, longitudinal, and cross-sectional studies with participants of different ages. Eight studies used the life grid at the beginning of the interview, four during the process, and one at the end. The ability to reduce recall bias, increase data reliability, establish rapport with participants, and ensure information accuracy were among the most commonly cited benefits of using the life grid in oral health research, particularly in oral cancer research. CONCLUSIONS: The life grid is a flexible tool used in a variety of structures and applications in oral health research. Positive impacts have been reported by both researchers and participants. This review highlights the potential of the life grid as a data collection and interview tool in oral health research and dental education.
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 | 0.081 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.032 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".