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Record W2935911342 · doi:10.18291/njwls.v9i1.113082

Employer Strategies for Preventing Mental Health Related Work Disability: A Scoping Review

2019· review· en· W2935911342 on OpenAlexaff
Ulrik Gensby, Hans Jørgen Limborg, Quenby Mahood, Christian Ståhl, Karen Albertsen

Bibliographic record

VenueNordic Journal of Working Life Studies · 2019
Typereview
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsInstitute for Work & Health
FundersFörsäkringskassan
KeywordsPsychosocialWork (physics)Mental healthIdentification (biology)Applied psychologyPsychologyBridge (graph theory)Knowledge managementMedicineComputer scienceEngineeringPsychiatry

Abstract

fetched live from OpenAlex

This study examined Nordic research on psychosocial work environment and disability management, specifically employer strategies for preventing work disability in common mental disorders (CMDs). A scoping review was performed to identify strategies across several research databases, alongside contact with content experts, hand-searching of non-indexed journals, and internet searches. Identification and selection of relevant studies, charting of data, and collating and summarizing of results was done using a six-step framework for conducting scoping reviews. Several key elements and knowledge gaps were identified in current prevention approaches and workplace initiatives across the included studies. We propose a program theory for workplace prevention of CMD-related work disability. The program theory may help specify employer strategies, and bridge activities with stakeholders outside the workplace.

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)0.011
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0160.012
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.295
GPT teacher head0.530
Teacher spread0.235 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations10
Published2019
Admission routes1
Has abstractyes

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