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The Clinical and Occupational Correlates of Work Productivity Loss Among Employed Patients With Depression

2004· article· en· W2073437818 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2004
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsAdler
FundersNational Center for Research ResourcesNational Institute of Mental HealthNIH Clinical CenterNational Institutes of Health
KeywordsDepression (economics)ProductivityWork productivityOccupational medicineClinical psychologyWork (physics)Occupational safety and healthPsychologyPsychiatryMedicineEnvironmental healthOccupational exposureEconomicsPathologyEngineering

Abstract

fetched live from OpenAlex

Employers who are developing strategies to reduce health-related productivity loss may benefit from aiming their interventions at the employees who need them most. We determined whether depression's negative productivity impact varied with the type of work employees performed. Subjects (246 with depression and 143 controls) answered the Work Limitations Questionnaire and additional work questions. Occupational requirements were measured objectively. In multiple regression analyses, productivity was most influenced by depression severity (P < 0.01 in 5/5 models). However, certain occupations also significantly increased employee vulnerability to productivity loss. Losses increased when employees had occupations requiring proficiency in decision-making and communication and/or frequent customer contact (P < 0.05 in 3/5 models). The Work Limitations Questionnaire can help employers to reduce productivity loss by identifying health and productivity improvement priorities.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.362
Teacher spread0.335 · 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