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Record W1973697499 · doi:10.2174/1876216601002010001

The Burden of Work Disability Associated with Mild Traumatic Brain Injury in Ontario Compensated Workers: A Prospective Cohort Study~!2009-11-16~!2010-02-21~!2010-03-16~!

2010· article· en· W1973697499 on OpenAlexaboutno aff
Vicki L. Kristman, Pierre Côté, Sheilah Hogg‐Johnson, J. David Cassidy, Dwayne Van Eerd, Marjan Vidmar, Mana Rezai, Richard Wennberg

Bibliographic record

VenueThe Open Occupational Health & Safety Journal · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
FundersInstitution of Occupational Safety and Health
KeywordsTraumatic brain injuryProspective cohort studyMedicineCohortPhysical therapyPhysical medicine and rehabilitationGerontologyPsychiatrySurgeryInternal medicine

Abstract

fetched live from OpenAlex

There is an extensive literature on the scale of, and risk factors for, stress at work, and outcomes associated with negative job characteristics and perceived stress. More recently, however, there has been a growing awareness that unemployment is harmful to health, and that work is beneficial and may be an effective way to improve health and wellbeing. However, there has been little attempt to draw together findings from this newer area. This paper gives an overview of current perspectives on work and its associations with improved health and well-being. It provides a framework for discussing the main themes which have emerged in this area. This framework allows consideration of the issues of conceptualisation, definition, approach and measurement which are important for future research. It is concluded that this area is still in its infancy, but clearly has the potential to make as influential a contribution as its negative counter-part. Currently, however, our understanding of what makes a good job and how we should conceptualise, study, and help workplaces provide that, is incomplete.

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)0.032
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0320.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0070.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.104
GPT teacher head0.455
Teacher spread0.350 · 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 teacher head, not a consensus.

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

Citations30
Published2010
Admission routes1
Has abstractyes

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