Work stress and recognition of need and intention to improve physical health
Bibliographic record
Abstract
The purpose of the study was to examine the association between work stress and the recognition of need and intention to improve physical health, data from 12,474 respondents to the 2013 to 2014 Canadian Community Health Survey was analyzed. For the explanatory measure, respondents were categorized as having most days at work as not at all, not very, a bit, or highly stressful. For the outcome, respondents were categorized as having no known need (none), a recognized need but no intention (recognition), or a recognized need and an intention (intention) to improve their physical health. A positive, dose-response relationship was found between work stress and recognition of need and intention to improve physical health. In multinomial logistic regression models adjusted for gender, age, education, income, hours worked per week, and dependent children, employees with the highest level of work stress had an increased odds of being in the recognition (odds ratio [OR] = 2.10, 95% confidence interval [CI] [1.38, 3.20]) and intention (OR = 2.57, 95% CI [1.89, 3.50]) groups (reference group: none) compared to employees with the lowest level of work stress. A greater emphasis on programs or interventions to support the initiation and maintenance of planned improvements may benefit workers with high work stress.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".