© 2014 by Mood Disorders Society of Canada.
Bibliographic record
Abstract
or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without prior written permission of Mood Disorders Society of Canada. Mood Disorders Society of Canada is pleased to present the latest publication in our popular public education series. Mental Health in the Workplace is based on the results of a survey of Canadian employees and employers talking about their perceptions of mental illness, either as experienced themselves, or as observed in their co-workers. The survey also spoke to managers who reported that they often did not know that one of their employees was struggling with a mental illness and, even if they knew, were unsure what to do to help. We know that one in five Canadians will have a mental illness or issue each year. We also know that unaddressed mental illness in the workplace cost Canadian businesses more than $20 billion in lost productivity (from absenteeism, presenteeism and turnover) in 2011. This handbook will help employers and employees create and sustain a mentally healthy workplace. It is also a guide to employers for the development of programs that will support employees who are experiencing mental illness – so that they can get well and return to full productivity.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.553 | 0.269 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".