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Record W2341714012 · doi:10.1177/2156869316642265

The “Work” of Workplace Mental Health

2016· article· en· W2341714012 on OpenAlexaffabout
Cindy Malachowski, Katherine Boydell, Peter H. Sawchuk, Bonnie Kirsh

Bibliographic record

VenueSociety and Mental Health · 2016
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConfidentialityNegotiationMental healthWork (physics)Public relationsMental illnessEthnographyPsychologyEmployee assistanceSociologyBusinessNursingPolitical sciencePsychiatryMedicine

Abstract

fetched live from OpenAlex

This article employs institutional ethnography (IE) inclusive of its distinctive epistemological stance to elucidate the institutional organization of the everyday work experience of the employee living with self-reported depression. The study was conducted within a large industrial manufacturing plant in Ontario, Canada. We discuss three institutionally organized processes that play a dominant role in coordinating the experiences of employees with self-reported depression: (1) employees’ work of managing and negotiating episodes of depression, (2) managers’ administrative work of maintaining privacy and confidentiality, and (3) the administrative work of authorizing illness. We shed light on how confidential medicalized disability management programs render managers ill prepared and inadequately trained to provide mental health support to their employees. Our findings inform advocacy efforts and facilitate both organizational and policy change to enhance services and supports for employees.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.036
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.002
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 head0.027
GPT teacher head0.382
Teacher spread0.355 · 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 designQualitative
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

Citations11
Published2016
Admission routes2
Has abstractyes

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