Why are Disclosure Decisions so Difficult? Understanding Factors that Encourage and Discourage Workers with a Chronic Disabling Condition from Disclosing Health Information at Work
Bibliographic record
Abstract
PURPOSE: The decision whether to disclose a disability at work is complex. Drawing on communication theories, we examined disclosure decision-making and how workers with disabling health conditions prioritized information that could simultaneously encourage and discourage disclosure. METHODS: An online, cross-sectional survey asked workers with physical and mental health/cognitive conditions creating job limitations (i.e., disability) at work about the health impacts on their employment, their disclosure goals, preferences, support availability, workplace culture, work context, and demographic information. Descriptive, multivariate, and latent profile analyses were used. RESULTS: Participants were 591 workers (51% men, 48% women, 1% non-binary) with physical (41%), mental health/cognitive (24%), or both groups of conditions (35%). Forty-two percent of participants had not disclosed health information or needs to their supervisor. Six profiles of decision patterns were identified: (1) little health impact, supports available; (2) some health impacts, positive support appraisals; (3) some health impacts, uncertain what to do; (4) some health impacts, considerable personal concerns; (5) little health impact, few concerns, few supports available; and (6) considerable health impacts but perceives many risks to sharing. Disclosure decisions often prioritized personal goals, preferences, and workplace culture over health impacts and support availability. Profiles were differentiated by health condition type and work context. CONCLUSION: Understanding how workers prioritize information when considering disclosing a disability at work has implications for organizational support practices and clinical efforts to support workers. It underscores that worker decisions go beyond health impacts and highlights the need for support resources to help workers address decision uncertainty and stress.
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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 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".