An Integrated Approach to Stakeholder Engagement
Bibliographic record
Abstract
The Wait Time Information System (WTIS) project was a complex change-management initiative. For the first time in Ontario, wait time data would be captured directly from clinician offices and publicly reported in an effort to improve access to care. The change meant using new technology, new business processes and, most importantly, a new dimension of accountability for making improvements within the health system. Success required engaging thousands of individuals at all levels of healthcare, many of whom were skeptical and resistant to the upcoming change, and subsequently gaining their support and motivating them to use the WTIS and its data. To achieve the level of stakeholder engagement that would be required to deploy and sustain the WTIS, the project team needed to address both the business reasons for change, and the emotional reactions to it. The team applied a three-pronged approach encompassing strong communications, compelling adoption efforts and hands-on training. Communication focused on awareness and education, ensuring that information was coordinated, consistent and transparent. Adoption efforts involved helping hospitals and users understand and prepare for the impact of change. Training provided hands-on practice to get people comfortable with using the system. This article explores how information management/information technology (IM/IT) projects can integrate communications, adoption and training to drive stakeholder engagement. It also provides insight around how, when used effectively, these functions can maximize limited resources and provide valuable benefits.
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 | 0.081 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".