The interactive process of negotiating workplace accommodations for employees with mental health conditions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".