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Record W2931341680 · doi:10.3899/jrheum.181201

Considerations for Evaluating and Recommending Worker Productivity Outcome Measures: An Update from the OMERACT Worker Productivity Group

2019· article· en· W2931341680 on OpenAlexvenueno aff
Suzanne Verstappen, Diane Lacaille, Annelies Boonen, Reuben Escorpizo, Catherine Hofstetter, Amye Leong, Sarah Leggett, Monique A. M. Gignac, Johan K. Wallman, Marieke M. ter Wee, Florian Berghea, Maria Agaliotis, Peter Tugwell, Dorcas Beaton

Bibliographic record

VenueThe Journal of Rheumatology · 2019
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
FundersPfizer
KeywordsMedicineProductivityOutcome (game theory)Group (periodic table)Physical therapyOperations managementEngineeringEconomic growth

Abstract

fetched live from OpenAlex

OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) Worker Productivity Group continues efforts to assess psychometric properties of measures of presenteeism. METHODS: Psychometric properties of single-item and dual answer multiitem scales were assessed, as well as methods to evaluate thresholds of meaning. RESULTS: Test-retest reliability and construct validity of single item global measures was moderate to good. The value of measuring both degree of difficulty and amount of time with difficulty in multiitems questionnaires was confirmed. Thresholds of meaning vary depending on methods and external anchors applied. CONCLUSION: We have advanced our understanding of the performance of presenteeism measures and have developed approaches to describing thresholds of meaning.

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)0.149
metaresearch head score (Gemma)0.206
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.788

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1490.206
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0120.007
Science and technology studies0.0020.003
Scholarly communication0.0060.008
Open science0.0050.004
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0020.002

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 head0.111
GPT teacher head0.424
Teacher spread0.313 · 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 designTheoretical or conceptual
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

Citations18
Published2019
Admission routes1
Has abstractyes

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