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Record W2674250848 · doi:10.1080/09638237.2017.1294738

Changes in the nature and intensity of stress following employment among people with severe mental illness receiving individual placement and support services: an exploratory qualitative study

2017· article· en· W2674250848 on OpenAlexaff
Christine Besse, Daniel Poremski, Vincent Laliberté, Éric Latimer

Bibliographic record

VenueJournal of Mental Health · 2017
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsPsychologyPsychological interventionCoping (psychology)Mental illnessMaladaptive copingPerceptionGrounded theoryMental healthQualitative researchSocial supportClinical psychologySocial psychologyApplied psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Most people with severe mental illness (SMI) want to work. Individual placement and support (IPS) programs have proven effective in helping them obtain and keep competitive jobs. Yet, practitioners often fear that competitive jobs might be too stressful. AIMS: To explore how the nature and intensity of stress experienced by IPS clients changed after the transition from looking for work to being employed. METHODS: Semi-structured interviews explored the experiences of 16 clients of an IPS program who had recently been competitively employed. Grounded theory was used to structure the analysis. RESULTS: Most participants reported that their stress level decreased once they found work. Stress following work was associated with fear of failure, pressure to perform and uncertainty. The support that people perceived in their return-to-work project, and where they were on their recovery journey, modulated their perception of stress. Many cited IPS as a source of support. CONCLUSIONS: Competitive work changed the nature of stress and was mostly associated with a decrease in stress level. Adjunctive interventions aiming to buffer self-stigma or help participants use more adaptive coping mechanisms may merit investigation.

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)0.002
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 score0.083
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.039
GPT teacher head0.386
Teacher spread0.347 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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