Roundtable: Parliamentarians and Mental Health: A Candid Conversation
Bibliographic record
Abstract
One in five Canadians will experience symptoms relating to mental illness in their lifetime. Yet, despite strides to destigmatise mental health conditions, people experiencing acute symptoms or episodes often feel as though they must struggle through alone and in silence. High-stress occupations, including those in parliamentary politics, are often places where these conditions first manifest or reappear due to certain triggers. The very public nature of the job and the continuing need to seek re-election tend to make politicians reluctant to disclose their mental health issues. In recent years, however, more parliamentarians appear to be coming forward, while in office, to speak openly about managing their mental health on the job. In this roundtable, three parliamentarians who have publicly disclosed their mental health conditions came together to talk about their experiences serving as parliamentarians while dealing with mental health conditions. With astonishing candour, they shared their stories and took the opportunity to talk to others in the same unique position about how they’ve persevered during trying times. The participants, while acknowledging the challenges of managing the conditions while in office also spoke of its positive effects in terms of giving them compassion, realism, and great perspective that can be used to excel at aspects of their jobs. This roundtable was held in November 2017.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.041 | 0.008 |
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".