Using Simulation-Based Learning (Gamified Educational Network) to Provide Micro-credentialing for Dementia Care Workers
Bibliographic record
Abstract
Background:Dementia care is a critical area of need in the community and institutional settings, with estimated one-third of seniors younger than 80 years of age with dementia living in institutional settings and this proportion increases to 42% for those 80 years and older in Canada. It is of critical importance to promote excellence and best practices in dementia care by preparing for well-trained dementia workforce through capacity building.Methods:This project developed a dementia care micro-credential education to enable competency development of new graduates and upskilling of workers through simulation-based learning. This micro-credential program leveraged interdisciplinary partnership, to develop nine core modules related to best practices in dementia care, facilitated with a Gamified Educational Network (GEN). GEN is an evidence-based learning management platform that provides learners with a simulated and immersive experience to engage them in a virtual learning environment that allows for rich experiential interaction with other users and its content.Outcome:Face and content validity was established by an inter-professional committee including geriatric psychiatry, nursing, social work, occupational therapy, behavioral therapy, knowledge mobilization and simulation education experts. Next phase will begin to establish construct validity. It is expected that GEN will have a positive impact on increasing learner’s motivation and engagement in the educational tasks, as well as improving learner’s competencies and outcomes through its multi-modal approaches, including gamification (usage of game-based elements in a non-game context to engage learners and promote learning), active observational practice, independent hands-on practice, case-based discussion, peer-to-peer assessment, expert facilitated feedback, skills debriefing and reflective practice.Conclusion:This micro-credential program will provide an enhanced dementia care curriculum for building capacity of existing workers, and those entering into the workforce to promote a dementia-friendly environment for older adults.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".