A mixed-methods study by Sana Siddiqui, last updated in April 2026, explores barriers to Employee Assistance Program (EAP) usage. It includes 30 semi-structured interviews with EAP-eligible employees aged 18–65 in Canada and the United States who had not used EAP services. The data was analyzed thematically and used to train a BERT-based NLP model to classify quotes by country.
Use Cases
- Train NLP models for text classification based on qualitative interview quotes.
- Compare perceived barriers to mental health services between Canadian and American workers.
- Analyze themes like structural barriers, stigma, and financial concerns in EAP utilization.
Strengths
- Data is based on 30 semi-structured interviews following COREQ reporting guidelines.
- Includes a cross-country comparison between Canada and the United States.
- Dataset is licensed under CC-BY-4.0 for open reuse.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- The dataset is very small at 9.5 KB, indicating limited scope.
Provenance
- Source
- Sana Siddiqui via figshare
- Collection Method
- Semi-structured interviews analyzed thematically and with a BERT-based NLP model.
- Time Range
- null
- Freshness
- Last updated 2026-04 03 17:53:19; freshness should be verified.
- Geography
- Canada and the United States