Pallium Canada's Curriculum Development Model: A Framework to Support Large-Scale Courseware Development and Deployment
Bibliographic record
Abstract
The need to improve access to palliative care across multiple settings and disease groups has been identified. This requires equipping health care professionals from many different professions, including physicians and nurses, among others, with basic palliative care competencies to provide a palliative care approach. Pallium Canada's Curriculum Development Framework supports the development, deployment, and dissemination, on a large scale, of multiple courses targeting health care professionals across multiple settings of care and disease groups. The Framework is made up of eight phases: (1) Concept, (2) Decision, (3) Curriculum Planning, (4) Prototype Development, (5) Piloting, (6) Dissemination, (7) Language and Cultural Adaptation, and (8) Ongoing Maintenance and Updates. Several of these phases include iterative cyclical activities. The framework allows multiple courses to be developed simultaneously, staggered in a production line with each phase and their corresponding activities requiring different levels of resources and stakeholder engagement. The framework has allowed Pallium Canada to develop, launch, and maintain numerous versions of its Learning Essential Approaches to Palliative Care (LEAP) courses concurrently. It leverages existing LEAP courses and curriculum materials to produce new LEAP courses, allowing significant efficiencies and maximizing output. This article describes the framework and its various activities, which we believe could be very useful for other jurisdictions undertaking the work of developing education programs to spread the palliative care approach across multiple settings, specialties, and disease groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".