Disclosing Mental Health: The Experience of Post Secondary Students Transitioning into a Career/Experiential Learning Environment
Bibliographic record
Abstract
The stigma associated with mental health and underrepresentation of students with disabilities in higher education is well documented (Barnard-Brak, Lechtenberger, and Tan 2010, p. 411). This presents a disclosure problem for post-secondary students with mental health challenges who are transitioning into a career or experiential learning environment. If students require workplace accommodations, they may find themselves faced with a decision to disclose mental health challenges to a faculty supervisor, career services practitioner or potential employer. Difficulties with mental health can be experienced because of a diagnosed mental health disability or be a symptom of another diagnosable disability.\nThe research begins to address a gap in the literature on these disclosure experiences and begins research into the lived experiences of students who, through known disclosures to faculty and other students, face abundant concerns about such disclosures. Using survey methodology, student experiences of these disclosure decisions, reactions and learnings were collected in two universities: Thompson Rivers and Wilfrid Laurier. Analysis of survey data identified dominant themes, differences facing different populations, and outliers. These results will be shared along with recommendations for universities, potential employers, and students themselves.\nWhen asked to identify their experiences with disclosing this information to employers, the study shows that 70% of students have “high” or “very high” fears of disclosing, and that these fears generated significant health challenges dues to their stress. These results are largely due to prior experiences with stigma, discrimination, and loss of opportunity. Almost all students experienced negative work or health consequences. And of the 58% who go on to disclose to a workplace supervisor, ¾ of them had a negative response. According to a study by Morris (2017), findings showed “that only 1 in 4 (25%) employees with disabilities [including mental health] who have an unmet need actually made the request to their employer or supervisor for it [and]…of those who did ask their employer or supervisor 40% were refused their request” (p. 10). The most common reason for not disclosing the need for workplace accommodations to an employer was levels of comfort and fear of negative outcomes (Morris, 2017, p.10).\nWhen considering the practice implications of these disclosure experiences, students indicated a need for improved resources, communication, connection, and education related to self-advocacy, self-disclosure, and accommodations. One survey participant expressed a need for universities to "make information related to self-disclosure/accommodations accessible and available to everyone because some students may not even know that these are options and what they would need to engage in these processes.” One way to support this request might be to include accessibility education within mandatory career management or career prep courses that are taken by all students pursuing coop education. Another resource might be an accommodations self-assessment tool to help students who are not sure what kinds of workplace supports they need (See Appendix 3). Students should also be provided with resources that can be accessed autonomously and confidentially, if required (TRU Deep Map Community Resources Hub, 2020, para 3). Resources for faculty, career services practitioners, and employers may include answers to frequently asked questions such as how to handle student disclosures, improve accessibility, determine essential job requirements, and implement accommodations. Such information could be provided online as well as through career professionals with workplace accessibility expertise within the university. Students who would like to access workplace accommodations will need to provide some level of disclosure.\nTherefore, to support students' transition into career and/or experiential learning environments, universities will need to develop resources and best practices that will create safer spaces to disclose. These resources should also include ways to support employers in becoming more accessible and inclusive for students with disabilities. Resources could include workshops on ways to become a more accessible employer and a matrix that provides accommodation suggestions for mitigating specific functional impacts (See Appendix 3).
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".