Purpose-Driven Design: A Case Report of a Knowledge Mobilization Portal
Bibliographic record
Abstract
Background & Need for Innovation: Synthesizing academic literature is a foundational skill in health professions education (HPE), enabling evidence-informed decision-making and continuous improvement. However, privileging one review type as the "gold standard" reinforces a narrow hierarchy of evidence, marginalizing alternative worldviews and synthesis approaches. Goal of Innovation: This innovation aimed to broaden understanding and legitimate use of diverse literature synthesis methods by developing an accessible knowledge mobilization portal to support learners, educators, and scholars across the HPE community. Steps Taken for Development and Implementation of Innovation: to showcase eight literature synthesis methods-the Literature Review Series (LRS). The Eco-Normalization Framework guided the design, implementation, and reflexive evaluation of the portal, aligning the innovation with contextual affordances and user needs. A collaborative, values-driven approach supported content co-creation, informed by lived experience, mutual trust, and a shared commitment to inclusivity. Evaluation of Innovation: The platform successfully launched and has been sustained through a network of contributors. Informal feedback and web analytics suggest positive engagement, and early adopters report its utility in teaching and research contexts. The innovation's resonance stems not only from its content but from the relationships and shared purpose underlying its development. Critical Reflection on Your Process: Key catalysts included: (1) friendship as an often-overlooked motivator in academic work; (2) trust and relationships that fostered momentum; and (3) a shared vision that anchored the innovation. These relational dimensions were as critical as the technical design in ensuring uptake and sustainability.
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 | 0.040 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".