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Record W2148588186 · doi:10.1155/2014/258614

Perspectives on Employment Integration, Mental Illness and Disability, and Workplace Health

2014· article· en· W2148588186 on OpenAlexaff
Nene Ernest Khalema, Janki Shankar

Bibliographic record

VenueAdvances in Public Health · 2014
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMental illnessMental healthPsychologyConceptual frameworkApplied psychologyPsychiatrySociologySocial science

Abstract

fetched live from OpenAlex

This paper reviews the literature on the interplay between employment integration and retention of individuals diagnosed with mental health and related disability (MHRD). Specifically, the paper addresses the importance of an integrative approach, utilizing a social epidemiological approach to assess various factors that are related to the employment integration of individuals diagnosed with severe mental illness. Our approach to the review incorporates a research methodology that is multilayered, mixed, and contextual. The review examines the literature that aims to unpack employers’ understanding of mental illness and their attitudes, beliefs, and practices about employing workers with mental illness. Additionally we offer a conceptual framework entrenched within the social determinants of the mental health (SDOMH) literature as a way to contextualize the review conclusions. This approach contributes to a holistic understanding of workplace mental health conceptually and methodologically particularly as practitioners and policy makers alike are grappling with better ways to integrate employees who are diagnosed with mental health and disabilities into to 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 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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.412
Teacher spread0.384 · 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 designNot applicable
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

Citations27
Published2014
Admission routes1
Has abstractyes

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