MétaCan
Menu
← Back to cohort
Record W4256330332 · doi:10.5596/c14-10

Creating Provincial and Territorial Search Filters to Retrieve Studies Related to Canadian Indigenous Peoples from Ovid MEDLINE

2014· article· en· W4256330332 on OpenAlexaffvenueabout
Sandy Campbell, Marlene Dorgan, Lisa Tjosvold

Bibliographic record

VenueJournal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsAlberta Library
Fundersnot available
KeywordsIndigenousTerminologyMEDLINESubject (documents)Government (linguistics)Library scienceGeographyGenealogyHistoryPolitical scienceComputer scienceLinguisticsLaw

Abstract

fetched live from OpenAlex

Introduction: Performing systematic review searches related to Canadian Indigenous peoples (First Nations, Inuit, and Métis), particularly in areas of public health, is difficult because Medical Subject Headings (MeSH) terms for both Indigenous peoples and geography do not retrieve all relevant articles in Ovid MEDLINE. Text–word searching for Canadian Indigenous peoples presents challenges in the varieties of names, spellings, and languages. A series of Canadian Indigenous peoples filters were designed to retrieve larger numbers of relevant articles. Objectives: The objectives of this work were (i) to create first-generation search filters that retrieve studies from the Ovid MEDLINE database related to Canadian Indigenous peoples, (ii) to determine whether or not the filters retrieve more records than do searches using the MeSH headings alone, and (iii) to determine how many of the additional records are relevant. Methods: Key terms describing both Canadian Indigenous peoples and Canadian geography were identified using government, historical, and ethnographic publications. Name lists included current and historical names in multiple languages, as well as local and settlement names, and names of linguistic groups. Filters, employing both text–word and MeSH terms were created for each province and territory, excluding Prince Edward Island. Search results were reviewed for false recalls related to terms with multiple meanings and groups of people whose lands straddle provincial and territorial borders. Revised searches were refined with additional terminology that implies the presence of Indigenous peoples. Duplicate records were removed from both the MeSH searches and the filter searches. Results from the MeSH searches were then removed from the results of the filter searches. The remaining results were analyzed for relevance. Results: Twelve Ovid MEDLINE filters were created and the challenges involved in creating them were documented. The filters increased recall by 58 articles, 464% over MeSH searches alone. Of the additional articles retrieved, 28 (100%) met the criteria for relevance. Discussion: The lists of challenges identified in the filter creation will assist other searchers in developing similar filters. The filters allow searchers to retrieve substantially more articles than is currently possible with the MeSH terms alone.

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 imitation

Not 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.

metaresearch head score (Codex)0.111
metaresearch head score (Gemma)0.319
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.587

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1110.319
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0710.048
Science and technology studies0.0040.003
Scholarly communication0.0060.005
Open science0.0060.006
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0150.002

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.

Opus teacher head0.083
GPT teacher head0.386
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
GenreMethods

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".

Quick stats

Citations5
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueJournal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada→Same topicMeta-analysis and systematic reviews→French-language works237,207→