Implementation of a certified mental health program for Imperial Oil Limited (IOL) Canada employees
Bibliographic record
Abstract
Background: It is noted that one in five Canadians experience a mental health problem or mental illness in a given year and every week 500,000 Canadians are unable to work due to mental illness. Mental illness accounts for 30% of disability claims and absenteeism and presenteeism from mental illness accounts for billions of dollars in lost productivity annually (Mental Health Commission of Canada, 2020). Purpose: To offer a certified mental health program for employees that reduces the stigma of mental illness, promotes well-being and enhances the organizations ability to support their employees. Additionally, it will assist to create a respectful and inclusive workplace, empower employees, and inspire them to seek support for their mental health concerns. Methods: An integrative literature review was completed to understand the impact of employee mental illness in the workplace and identify programs that can be implemented in the workplace to promote mental health. Consultations were completed with key stake holders to obtain their thoughts on the company’s mental health culture. Lastly, an environmental scan was carried out to provide information on programs that are available and have been implemented in Canada. Results: Through the completion of the practicum it was evident that poor mental health had a negative impact on employee’s ability to work effectively. The lost productivity and expense associated with disability also costs companies millions of dollars annually. Conclusion: Implementing a Certified Mental Health Program, The Working Mind, has the ability to reduce stigma and increase resiliency, thus improving employee well-being, increasing productivity and reducing the cost associated with absenteeism and disability claims.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".