Towards a Common Vision for Innovation: Making Sense of Complexity in a Health Sciences Program
Bibliographic record
Abstract
The growing use of digital educational technologies in higher education has seen considerable change resulting in significant institutional energies directed towards maintaining currency with today’s emerging trends. The move to digital transformation is an inevitable assumption and generally positively accepted by academia. Despite this, technology integration has emerged in an ad hoc and reactive fashion rather than purposeful and strategic. This Organizational Improvement Plan (OIP) addresses the need for a shared vision for technology adoption across a health sciences program in a mid-sized institution. Although faculty participate enthusiastically in developing curricular initiatives, their roles and engagement with technology visioning are often void of their collective voices. The theoretical concepts of sensemaking and learning culture offer insight into the complexity of connecting technology to learning pedagogy. Central to developing capacity requires facilitating meaningful connections between users about the technology and the implications to practice. This OIP builds upon the need for a collaborative lens that acknowledges cultural nuances and individual empowerment. Key in the success of leading the process will be the enactment of adaptive and transformational leadership, where the approach for change is modelled in a collaborative and supportive manner. The change implementation plan of the proposed change is fostered by the dual application of Cawsey et al.’s (2016) Change Model and Kotter’s eight-stage process (2012). Ultimately, this OIP will result in an integrated visionary approach to technology adoption across a health science program.
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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 | 0.074 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.027 | 0.052 |
| Scholarly communication | 0.034 | 0.028 |
| Open science | 0.003 | 0.046 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".