Prevention Is the Best Medicine : Development of a Work and Health Toolkit for New Immigrants Using Settlement Services in Ontario
Bibliographic record
Abstract
New immigrants to Ontario have a greater probability of being employed in jobs with a higher number of occupational health and safety (OHS) hazards. In addition, recent immigrants may have higher risks of work injuries and be less likely to access compensation after injury. Despite these issues, information provided to new immigrants about OHS and workers’ compensation (WC) is often informal and fragmented. In this paper, we discuss the development of a tool to share information about OHS and workers’ compensation with newcomers using settlement services. While acknowledging that employers and regulatory bodies also have responsibility for OHS, new immigrants must have the necessary resources to protect themselves from harm in the workplace, and know what to do in the event of an injury and how to access services and support.Building on previous research, we completed a national scan that reviewed the existing safety resources for recent immigrants entering the Canadian workforce. Then, in conjunction with an Advisory Committee, we developed a toolkit covering the general components of OHS in Ontario and the Ontario Workers’ Compensation system. Once the toolkit was developed, focus groups were conducted with educators and new immigrant learners from a large settlement organization in Toronto which provided feedback on the tools.In the method and results section, we will discuss the toolkit development process and the focus group results. In the focus groups, there was agreement that it was important to introduce this information in programs that prepared newcomers for employment, with many people stressing that information had to be accompanied by efforts to increase employer knowledge and compliance. Settlement staff identified a lack of time and knowledge about OHS and WC as barriers to delivering this information. Both groups thought it was important that work-related mental health issues be addressed in the material. Material had to be delivered in a way that could incorporate class discussion, exercises and questions. Settlement staff wanted some guidance around how to address difficult questions and issues related to these topics.The development of this toolkit has the potential to improve existing services offered to new immigrants and to increase immigrant worker knowledge about health and safety before a workplace problem or injury occurs. Such knowledge can help reduce the risk of injury and lead to more positive social, health and economic outcomes. We end this paper by discussing dissemination activities and recommendations for future work in this area.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".