Help! I need somebody: Help-seeking among workers with work-related mental disorders
Bibliographic record
Abstract
Abstract Purpose: Worker mental health has emerged as one of the most significant challenges in contemporary workplaces. Knowing what intervention is effective is important to help workers adapt to mental health problems but connecting workers to helpful resources is just as important and perhaps more of a challenge. With the multiple stakeholders involved, mental health problems arising in the workplace poses specific challenges to help-seeking. The present study sought to understand the personal and contextual influences on help-seeking among workers with work-related mental health problems. Methods: A qualitative methodology was employed utilizing purposive sampling to conduct semi-structured interviews with individuals (n=12) from various occupational backgrounds who had experienced a work-related (self-declared) mental health injury. Interpretative phenomenological analysis and thematic content analysis were combined to analyze the data. Results: Three main themes emerged including: 1) self-preservation through injury concealment and distancing themselves from workplace stressors to minimize/avoid internal and external stigma, 2) fatigue relating to complex help-seeking pathways, accumulation of stressors, eroding the worker’s ability to make decisions regarding supports, and 3) (mis)trust in the people and processes involving dual relationships with help providers and the workplace and trust in peer referrals and networks for help. Conclusions: Findings suggest the need to educate workplace parties such as supervisors on mental health and pathways to help, simplifying pathways to service and removing barriers to help seeking including stigmatizing behaviours. Future quantitative and intervention research on workplace mental health should integrate pathways to help into models and frameworks.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".