USING MICRO-CREDENTIAL TO ADVANCE LONG-TERM CARE STAFF EXPERTISE IN PALLIATIVE CARE FOR PEOPLE WITH DEMENTIA
Bibliographic record
Abstract
Abstract Background Ontario Tech University partnered with experts from Alzheimer’s Society of Canada (Durham Region of Ontario) and Ontario Shores Centre for Mental Health Sciences to develop a micro-credentialing certificate program in dementia care. This study aims to evaluate the usability and applicability of using micro-credential learning to advance long-term care staff’s expertise in providing palliative care for people with dementia. Methods Registered Practical Nurses (RPNs), Registered Nurses (RNs), Nurse Practitioners (NP) and Personal Support Workers (PSWs) (n=19) who worked in long-term care homes located in Southern Ontario completed a one-day training to pilot-test a gamified micro-credential education related to palliative care for people with dementia. Upon completion of the training, a quantitative pre-assessment and post-assessment were used to evaluate learner’s perceived knowledge, skill, attitude and confidence as measured by the Educational Self-Efficacy Scale. Results Overall, there was a statistically significant increase in knowledge after the micro-credential training (P<.0001), with a mean increase of 3.2 more questions answered correctly. Focus group findings revealed four key themes: opportunities of using gamified simulation to promote learner’s engagement; lack of palliative care training to support people with dementia; need to strengthen workplace training using micro-credential for upskilling; and meaning of palliative care in long-term care sector. Conclusion Palliative care is recognized to be an important part of work in long-term care as residents are often admitted with complex and life-threatening conditions. Participants highlighted the feasibility of enhancing their knowledge and competence in palliative care through specialized training using gamified, simulation learning through micro-credential training.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".