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Record W2156871998 · doi:10.1177/1049732312466296

When Health Care Workers Experience Mental Ill Health

2012· article· en· W2156871998 on OpenAlexafffund
Sandra Moll, Joan M. Eakin, Renée‐Louise Franche, Carol Strıke

Bibliographic record

VenueQualitative Health Research · 2012
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsSimon Fraser UniversityUniversity of TorontoMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMental healthSilenceEthnographyPsychologyHealth careMental illnessNursingProductivityWork (physics)Public relationsSociologyMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Based on findings from an institutional ethnography in a large mental health organization, we explore how institutional forces shape the experiences of health care workers with mental health issues. We interviewed 20 employees about their personal experiences with mental health issues and work and 12 workplace stakeholders about their interactions with workers who had mental health issues. We also reviewed organizational texts related to health, illness, and productivity. In analyzing transcripts and texts, silence emerged as a core underlying process characterizing individual and organizational responses to employees with mental health issues. Silence was an active practice that took many forms; it was pervasive, complex, and at times, paradoxical. It served many functions for workers and the organization. We discuss the theoretical and practical implications of the findings for workers with mental health issues.

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)0.043
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0430.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0110.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0010.002

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 head0.432
GPT teacher head0.668
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

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

Citations60
Published2012
Admission routes2
Has abstractyes

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