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Record W2986760849 · doi:10.5014/ajot.2019.73s1-po4052

Working Together: Interdisciplinary Interventions to Manage Fatigue Symptoms of Cancer Survivors Returning to Work

2019· article· en· W2986760849 on OpenAlexaff
Naomi Dolgoy, Margaret L. McNeely

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsAlberta Cancer FoundationUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsPsychological interventionCancerCancer survivorWork (physics)GerontologyPhysical activityCancer-related fatigueMedicinePsychologyVocational educationPhysical therapyPsychiatryEngineering

Abstract

fetched live from OpenAlex

Date Presented 04/05/19 CRF is the most commonly reported ongoing negative symptom impacting cancer survivors. Cancer survivors experience difficulty managing work after cancer, citing CRF as a major barrier to workability. Currently, functional interventions are highly limited. This poster will introduce results from a research pilot, combining physical exercise with functional activities for cancer survivors experiencing CRF and progressing to their previous vocational roles Primary Author and Speaker: Naomi Dolgoy Contributing Authors: Margaret McNeely

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.062
GPT teacher head0.404
Teacher spread0.342 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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