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Record W4230642409 · doi:10.1093/geront/gnw162.2469

DEVELOPMENT OF A WORKFORCE COMPETENCY BASED TRAINING IN GEROPSYCHOLOGY

2016· article· en· W4230642409 on OpenAlexaff
Andrew Wister, Barbara S. Mitchell, Laura Kadowaki, Simon Gerontology, Stephanie P. Wladkowski, Jennifer H. Lindquist

Bibliographic record

VenueThe Gerontologist · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicForeign Language Teaching Methods
Canadian institutionsFraser Institute
FundersU.S. Department of Veterans Affairs
KeywordsWorkforceTraining (meteorology)Workforce developmentMedical educationPsychologyBusinessMedicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

function measures based on category switching or inhibition measures showed significant practice effects; however, there was variability in the significance of practice effects among individual tests within domains.Tests with a visualspatial component, even if that was not the main focus of the test, tended to have larger practice effects.Thus, although practice effects are common after nearly 6 years, they differ depending on the cognitive tests being assessed.Failing to take differing practice effects into account may result in misestimation of age-related cognitive declines or mild cognitive impairment.

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)0.005
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.133
GPT teacher head0.404
Teacher spread0.271 · 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
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

Citations0
Published2016
Admission routes1
Has abstractyes

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