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Record W2970116080 · doi:10.3233/wor-213459

The interactive process of negotiating workplace accommodations for employees with mental health conditions

2021· article· en· W2970116080 on OpenAlexaffabout
Sabrina Hossain, Sandra Moll, Emile Tompa, Rebecca Gewurtz

Bibliographic record

VenueWork · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsInstitute for Work & HealthMcMaster University
Fundersnot available
KeywordsNegotiationMental healthAccommodationProcess (computing)PsychologyPublic relationsReasonable accommodationQualitative researchPerspective (graphical)Applied psychologyBusinessPolitical scienceSociologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Implementing workplace accommodations is an effective means of retaining employees with mental health conditions. However, the process is poorly understood and poorly documented. OBJECTIVE: The purpose of this research is to explore the interactive process of negotiating workplace accommodations from the perspective of employees with mental health conditions and workplace stakeholders. METHODS: We interviewed employees across Canada who self-identified as having a mental health condition requiring accommodations, and six stakeholders at various workplaces across Canada who are involved in providing accommodations. Data were analyzed using a qualitative descriptive approach to identify key themes. RESULTS: The findings highlight that the process of negotiating accommodations is non-linear, interactive, and political. The process is shaped by organizational and political factors and collaboration between stakeholders. CONCLUSIONS: The negotiation process is a combination of social, relational and political factors. Clear and accessible accommodation policies, workplace awareness and specific workplace training on how to implement accommodations are needed to optimize the accommodation process for all involved.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.030
GPT teacher head0.400
Teacher spread0.369 · 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 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

Citations13
Published2021
Admission routes2
Has abstractyes

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