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Record W4408281252 · doi:10.59210/v3tr8640

Work is therapy, not a goal in itself, for people with chronic pain.

2023· article· en· W4408281252 on OpenAlexaff
Morten Hoegh, Duygu Biricik Gulseren

Bibliographic record

VenuePain and Rehabilitation · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsYork University
Fundersnot available
KeywordsExcuseFeelingWeaknessSick leaveChronic painWork (physics)Physical therapyRehabilitationMedicineObstaclePain therapyPsychologyPsychotherapistPhysical medicine and rehabilitationSocial psychologySurgeryPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Pain has long been seen as an obstacle for work and many people living with chronic pain feel stigmatised in the workplace (e.g., feeling that their pain is experienced as an excuse not to work or as a sign of weakness)3. Although musculoskeletal pain is the leading cause of sick leave in Europe with estimated costs of up-to 2% of the GDP4, putting people with pain on sick leave is against general recommendations6,11. In this commentary, we will introduce the work-as-therapy concept as a plausible rehabilitation method and explain how it can benefit patients and their employers.

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)0.010
metaresearch head score (Gemma)0.025
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: none
Teacher disagreement score0.028
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.020
Scholarly communication0.0060.009
Open science0.0030.005
Research integrity0.0280.030
Insufficient payload (model declined to judge)0.0060.002

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 head0.009
GPT teacher head0.268
Teacher spread0.259 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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