Economic evaluation of interventions for occupational health and safety : developing good practice
Bibliographic record
Abstract
Undertaking economic evaluations of occupational health and safety interventions can be difficult for a number of reasons. This is reflected by the significant lack of evidence on their cost-effectiveness. Particular challenges include: complex labour legislation, differences in the perception of health risks associated with work experiences amongst workplace parties and policy makers, costs and consequences being borne by different stakeholders in the system, conflicting incentives and priorities between the multiple stakeholders, lack of consensus about what ought to count as a benefit or cost of intervening or not intervening, multiple providers of indemnity and medical care coverage, and industry-specific human resources practices that make it difficult to identify all work-related illnesses and injuries. Advancement of the application of economic evaluation methods in this literature is further hindered by the fact that most methods books are designed for use in a clinical setting and cannot be easily applied to the workplaces. In the face of such barriers, it is not surprising that few studies of occupational health and safety interventions contain an economic evaluation. This book aims to lay the foundations for a systematic methodology of economic evaluation of workplace interventions, by identifying the main barriers to research of high quality and practical relevance, and proposing a research strategy to overcome them. Context chapters provide a wealth of background material ranging from a presentation of the broad conceptualization of work and health, to suggestions for strategies in confronting the dearth of data often experienced by occupational health and safety researchers. The institutional and regulatory approaches in different international jurisdictions are covered in one of the context chapters. Specific topic chapters delve into the principles and application of economic evaluation methods relevant to workplaces and system level interventions. Study design, type of analysis, costs, consequences, uncertainty, and equity are all covered, providing guidance on meeting many analytical and decision-making challenges. The final chapter synthesizes the summaries, conclusions, challenges and recommendations from across the book, presenting the synthesis as a reference case. Contributors to this volume - Benjamin C. Amick III, Institute for Work & Health, Toronto, Canada Phil Bigelow, Institute for Work & Health, Toronto, Canada Alan Clayton, Australian National University - National Research Centre for Occupational Health and Safety Regulation, Canberra, Australia Donald C. Cole, Institute for Work & Health, Toronto, Canada Richard Cookson, Department of Social Policy and Social Work, University of York, Heslington, York, UK Anthony J. Culyer, Department of Economics and Related Studies, University of York, Heslington, York, United Kingdom Carolyn S. Dewa, Work and Well-being Research and Evaluation Program, Centre for Addiction and Mental Health, Toronto, Canada Roman Dolinschi, Institute for Work & Health, Toronto, Canada Peter Dorman, Department of Environmental Studies, The Evergreen State College, Olympia, United States William Gnam, Institute for Work & Health, Toronto, Canada Michel Grignon, Departments of Gerontology and Economics, McMaster University, Hamilton, Canada Jeffrey S. Hoch, Centre for Research on Inner City Health, The Keenan Research Centre in the Li Ka Shing Knowledge Institute of St. Michael's Hospital, Toronto, Canada Ulrike Hotopp, Department for Business, Enterprise and Regulatory Reform, London, UK Birgit Koeper, Federal Institute for Occupational Safety and Health (BAuA), Dortmund, Germany Thomas Kohstall, Economy and Central Duties Department, BGAG Institute Work & Health, Dresden, Germany Audrey Laporte, Department of Health Policy, Management and Evaluation, Faculty of Medicine, University of Toronto, Canada John Mendeloff, Graduate School of Public and International Affairs, University of Pittsburgh, Pittsburgh, United States Cameron Mustard, Institute for Work & Health, Toronto, Canada Karen Niven, Karen Niven Consulting Ltd., Gairneybank, Kinross, UK Claire de Oliveira, Institute for Work & Health, Toronto, Canada Lynda Robson, Institute for Work & Health, Toronto, Canada Mark Sculpher, Centre for Health Economics, University of York, Heslington, York, UK Sandra Sinclair, Institute for Work & Health, Toronto, Canada Emile Tompa, Institute for Work & Health, Toronto, Canada
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 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 | 0.077 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".