Embracing Complexity to Foster Program Adaptation at a College in Ontario
Bibliographic record
Abstract
The knowledge, skills, values, and confidence needed to practice dental hygiene safely, effectively, and ethically are evolving. Healthcare and the higher education landscapes are becoming increasingly complex. Despite the increased curricular competencies, emerging research about positive aspects of baccalaureate dental hygiene education, and out-of-province trends towards degree education, Bayview College continues to only offer a diploma credential that is specific to dental hygiene. Diverse views have polarized faculty on how to adapt the dental hygiene program so that it better meets professional, community, and societal needs. This Organizational Improvement Plan explores the organizational context at Bayview College and proposes a strategy to address the problem of practice, which is the lack of a shared vision about the evolution of its dental hygiene program. As a faculty member at the institution, I lay out the path to guide the change process. The plan’s overarching leadership framework, complexity leadership theory, combined with Stacey’s complexity theory, Olson and Eoyang’s conditions for self-organization, and the Plan-Do-Study-Act cycle reflect the changing environmental circumstances and complex adaptive systems that make up the Bayview College community. In addition, the selected strategy, an appreciative inquiry initiative, will foster stakeholder engagement, emergence, and creative problem solving as a means to address the identified problem of practice. I incorporate detailed plans for implementation, monitoring and evaluating, and communicating the need for change. I conclude with a path forward on how the vision can be actualized within the organization and set the foundation for future change.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".