Project Data for "Factors associated with the evolution in the use of Medical Assistance in Dying"
Bibliographic record
Abstract
This dataset contains search strategies for electronic bibliographic databases (Medline via Ovid, Embase via Ovid, PsycInfo via Ovid, CINAHL Complete via EBSCO, Web of Science Core Collection, Sociological Abstracts via ProQuest, Social Sciences Abstracts via EBSCO, ProQuest Dissertations and Theses Closed, Érudit and Persée). Database searches were run February 28. 2025. Strategies for grey literature sources will be added in the future. The final concepts retained for the search strategies were: (1) medical assistance in dying, (2) requests, provision, or statistical data pertaining to cases and (3) jurisdictions in which MAiD has been allowed for at least 5 years. // Ce jeu de données contient des stratégies de recherche pour les bases de données bibliographiques (Medline via Ovid, Embase via Ovid, PsycInfo via Ovid, CINAHL Complete via EBSCO, Web of Science Core Collection, Sociological Abstracts via ProQuest, Social Sciences Abstracts via EBSCO, ProQuest Dissertations and Theses Closed, Érudit et Persée). Les recherches dans les bases de données ont été effectuées le 28 février 2025. Des stratégies pour les sources de littérature grise seront ajoutées ultérieurement. Les concepts retenus pour les stratégies de recherche étaient les suivants : (1) l’aide médicale à mourir, (2) les demandes, les données relatives à l’aide médicale à mourir ou les données statistiques concernant les cas, et (3) les juridictions où l’aide médicale à mourir est autorisée depuis au moins cinq ans.
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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.121 | 0.025 |
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".