Evaluating the implementation of health and safety innovations under a regulatory context: A collective case study of Ontario’s safer needle regulation
Bibliographic record
Abstract
BACKGROUND: Implementation effectiveness models have identified important factors that can promote the successful implementation of an innovation; however, these models have been examined within contexts where innovations are adopted voluntarily and often ignore the socio-political and environmental context. In the field of occupational health and safety, there are circumstances where organizations must adopt innovations to comply with a regulatory standard. Examining how the external environment can facilitate or challenge an organization's change process may add to our understanding of implementation effectiveness. The objective of this study is to describe implementation facilitators and barriers in the context of a regulation designed to promote the uptake of safer engineered medical devices in healthcare. METHODS: The proposed study will focus on Ontario's safer needle regulation (2007) which requires healthcare organizations to transition to the use of safer engineered medical devices for the prevention of needlestick injuries. A collective case study design will be used to learn from the experiences of three acute care hospitals in the province of Ontario, Canada. Interviews with management and front-line healthcare workers and analysis of supporting documents will be used to describe the implementation experience and examine issues associated with the integration of these devices. The data collection and analysis process will be influenced by a conceptual framework that draws from implementation science and the occupational health and safety literature. DISCUSSION: The focus of this study in addition to the methodology creates a unique opportunity to contribute to the field of implementation science. First, the study will explore implementation experiences under circumstances where regulatory pressures are influencing the organization's change process. Second, the timing of this study provides an opportunity to focus on issues that arise during later stages of implementation, a phase during the implementation cycle that has been understudied. This study also provides the opportunity to examine the relevance and utility of current implementation science models in the field of occupational health where the adoption of an innovation is meant to enhance the health and safety of workers. Previous work has tended to focus almost exclusively on innovations that are designed to enhance an organization's productivity or competitive advantage.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".