Workplace support for employees with cancer / by Behdin Nowrouzi.
Bibliographic record
Abstract
PURPOSE: The aim of this study was to survey human resource personnel about how their northeastern Ontario workplaces assist employees with cancer. METHODS: This crosssectional study sent surveys to 255 workplaces in northeastern Ontario with 25 or more employees from December 2007 to April 2008. There were 101 respondents (39.6% response rate). Logistic regression modelling was used to identify factors associated with more or less workplace support. More or less workplace support was defined as those workplaces that provided employees with paid time for medical appointment, and offered a return to work meeting and reduced hours for employees with cancer. Factors considered in the model \nincluded: organizational size, geographic location (e.g., urban or rural) and workplace type (e.g., private versus public sector). RESULTS: The majority of participants were female (67.4%) and ranged in age from 25 to 70. Respondents reported working for organizations that ranged in size \nfrom 25 to over 9000 employees. In the logistic regression model, large organizational size (OR, 6.97, 95% Cl, 1.34 - 36.2) and public sector (e.g., governmental bodies, education boards and non-profit organizations) (OR, 4.98, 95% Cl, 1.16 - 21.3) were associated with employer assistance. Public sector employers were 5 times more likely, while organizations with more \nthan 50 employers were almost 7 times more likely to provide employer assistance. The geographic location (i.e., urban or rural workplace) was not associated with employer assistance. \nCONCLUSIONS: This study indicates how employers are assisting employees with cancer in their northeastern Ontario workplaces and provides support to foster supportive professional relationships during a difficult period in a worker?s life which necessitates concurrent balance of work life, dealing with a chronic illness, and family responsibilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 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 teacher head, 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".