Evidence map›Paper›PMID 41140950›Full record

ArticleDialogues in health2025

Overhearing hushed voices: Using unobtrusive methods to uncover the work-related sentiments of people with epilepsy.

Asha Rao, Surendra Sarnikar

Abstract read
In one paragraph

Article in Dialogues in health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Asha RaoDepartment of Management, California State University East Bay, 25800 Carlos Bee Blvd, Hayward, CA 94542, USA.
Surendra SarnikarDepartment of Management, California State University East Bay, 25800 Carlos Bee Blvd, Hayward, CA 94542, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Despite high unemployment and discrimination, disability is often overshadowed by race and gender in workplace diversity discussions. This study sought to uncover the work-related sentiments of people with epilepsy (PWE) in their own voices. Epilepsy is a stigmatized invisible disability wherein first-person accounts are difficult to uncover because of stigma. Hence, the discussion on accommodation, inclusion and access for PWE is driven by others, rather than people with epilepsy. By hearing from PWE, workplace solutions can be more relevant to their needs. Design: We use unobtrusive data mining of discussions by PWE by studying anonymous posts on the Epilepsy Foundation community forum over a 16-year period. We examine their sentiments, beliefs, and emotions impacting work for people with epilepsy (PWE), and analyzed perceptions of discrimination to understand what organizations can do better to be inclusive and supportive at work. Findings: We found that PWE sentiments around work were more positive compared to nonwork sentiments. Their work sentiments were analytical, achievement oriented and associated with positive emotions. Work was associated with love and affiliation. However, PWE posts were broadly also more anxious, health centered and tentative reflecting their fears around their health and stigma in employment. Originality: This study is unique in using unobtrusive data mining techniques to analyze the challenges faced by people with disabilities. To our knowledge, there is no such study of adults with epilepsy and the workplace. The methodology used reduces response bias. Questions about disability are difficult to answer for people who face stigma around their disability.

Indexed as

DisabilityEpilepsyInclusionStigmaUnobtrusive research

Identifiers

PMID41140950
PMCPMC12546669

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.