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Record W4381839344 · doi:10.1177/15394492231180833

Fatigue as an Understudied Barrier to Participation in Life Roles

2023· article· en· W4381839344 on OpenAlexaff
Janet L. Poole, Susan L. Murphy, Erin R. Foster, Alix G. Sleight, Toni Van Denend, Arash Asher, Kristine Carandang, Marcia Finlayson, Donald Fogelberg, Heather Jim, Aileen Ledingham, Tanya Packer, Matthew Plow, Katherine Preissner, Stephen Wechsler, Daniel Whibley

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsDalhousie UniversityQueen's University
Fundersnot available
KeywordsOccupational therapySocializationPsychological interventionPsychologyFoundation (evidence)MedicineMedical educationEngineering ethicsNursingPolitical scienceEngineeringPhysical therapySocial psychology

Abstract

fetched live from OpenAlex

Fatigue is one of the most burdensome and disabling symptoms in numerous acute and chronic conditions and is associated with reduced participation in all aspects of daily life, for example, parenting, employment, and socialization. Historically, occupational practitioners played key roles in fatigue management by creating and implementing interventions. The American Occupational Therapy Foundation convened a Planning Grant Collective workshop with the goal to develop collaborative research ideas and proposals to advance the understanding and management of fatigue. Participants from occupational therapy and other disciplines, with expertise with fatigue or who were conducting research on fatigue, participated in a 3-day virtual workshop. Four main topics emerged: implementation science, treatment taxonomy, trial design and comparative effectiveness, and phenotyping. This white paper describes the proceedings, summarizes the discussions, and outlines recommendations from the PGC workshop on fatigue.

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 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)0.016
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.587
GPT teacher head0.661
Teacher spread0.074 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations9
Published2023
Admission routes1
Has abstractyes

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