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Record W2893606582 · doi:10.1080/0142159x.2018.1498589

2018 Ottawa consensus statement: Selection and recruitment to the healthcare professions

2018· article· en· W2893606582 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMedical Teacher · 2018
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsSelection (genetic algorithm)GlobeContext (archaeology)Diversity (politics)Health careEngineering ethicsPublic relationsPolitical scienceQuality (philosophy)DisciplineSociologyPsychologySocial scienceEpistemologyComputer scienceEngineeringLawGeography

Abstract

fetched live from OpenAlex

Selection and recruitment into healthcare education and practice is a key area of interest for educators with significant developments in research, policy, and practice in recent years. This updated consensus statement, developed through a multi-stage process, examines future opportunities and challenges in selection and recruitment. There is both a gap in the literature around and a compelling case for further theoretical and empirical literature to underpin the development of overall selection philosophes and policies and their enactment. More consistent evidence has emerged regarding the quality of different selection methods. Approaches to selection are context-dependent, requiring the consideration of an institution's philosophy regarding what they are trying to achieve, the communities it purports to serve, along with the system within which they are used. Diversity and globalization issues continue to be critically important topics. Further research is required to explore differential attainment and explain why there are substantial differences in culturally acceptable ways of approaching diversity and widening access. More sophisticated evaluation approaches using multi-disciplinary theoretical frameworks are required to address the issues. Following a discussion of these areas, 10 recommendations are presented to guide future research and practice and to encourage debate between colleagues across the globe.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.749
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0210.000

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 head0.139
GPT teacher head0.454
Teacher spread0.315 · 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