The web of silence: a qualitative case study of early intervention and support for healthcare workers with mental ill-health
Bibliographic record
Abstract
BACKGROUND: There is a high rate of stress and mental illness among healthcare workers, yet many continue to work despite symptoms that affect their performance. Workers with mental health issues are typically ostracized and do not get the support that they need. If issues are not addressed, however, they could become worse and compromise the health and safety, not only of the worker, but his/her colleagues and patients. Early identification and support can improve work outcomes and facilitate recovery, but more information is needed about how to facilitate this process in the context of healthcare work. The purpose of this study was to explore the key individual and organizational forces that shape early intervention and support for healthcare workers who are struggling with mental health issues, and to identify barriers and opportunities for change. METHODS: A qualitative, case study in a large, urban healthcare organization was conducted in order to explore the perceptions and experiences of employees across the organization. In-depth interviews were conducted with eight healthcare workers who had experienced mental health issues at work as well as eight workplace stakeholders who interacted with workers who were struggling (managers, coworkers, union leaders). An online survey was completed by an additional 67 employees. Analysis of the interviews and surveys was guided by a process of interpretive description to identify key barriers to early intervention and support. RESULTS: There were many reports of silence and inaction in response to employee mental health issues. Uncertainty in identifying mental health problems, stigma regarding mental ill health, a discourse of professional competence, social tensions, workload pressures, confidentiality expectations and lack of timely access to mental health supports were key forces in preventing employees from getting the help that they needed. Although there were a few exceptions, the overall study findings point to many barriers to supporting employees with mental health issues. CONCLUSIONS: In order to address the complex knowledge, attitudinal, interpersonal and organizational barriers to action, a multi-layered knowledge translation strategy is needed, that considers not only mental health literacy and anti-stigma interventions, but addresses the unique context of the work environment that can act as a barrier to change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".