MétaCan
Menu
Back to cohort
Record W3024698145 · doi:10.1177/084456211504700401

Message from the Senior Editors

2015· article· en· W3024698145 on OpenAlexaffvenueabout
Andrea Baumann, Dina Idriss-Wheeler, Jennifer Blythe, Paul Rizk

Bibliographic record

VenueCanadian Journal of Nursing Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsWorkforceHealth careUsabilityNursingPolitical scienceLibrary scienceSociologyManagementMedicine

Abstract

fetched live from OpenAlex

In Canada and elsewhere, the case for hiring internationally educated nurses (IENs) has not been adequately made and guidance for employers is lacking. The Web site Internationally Educated Nurses: An Employer's Guide, launched in 2012, is intended to provide healthcare employers in Ontario with comprehensive information on the hiring and integration of IENs. An evaluation framework and mixed methods design were used to determine the usability of the site in relation to its goal. Convenience sampling was employed to select participants representing specified users (i.e., healthcare employers). Overall evaluation of usability was positive. Participants indicated that it raised their awareness of the advantages of hiring and integrating IENs to address shortages, increase workforce diversity, and provide culturally competent care. Future projects should focus on collaboration with employers to increase the uptake of IENs.

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)0.004
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0060.005
Open science0.0030.002
Research integrity0.0200.025
Insufficient payload (model declined to judge)0.0310.022

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 head0.290
GPT teacher head0.557
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations3
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Nursing ResearchSame topicGlobal Health Workforce IssuesFrench-language works237,207