Changing the Impact of Nursing Assistants’ Education in Seniors’ Care: the Living Classroom in Long-Term Care
Bibliographic record
Abstract
BACKGROUND: Evidence-informed care to support seniors is based on strong knowledge and skills of nursing assistants (NAs). Currently, there are insufficient NAs in the workforce, and new graduates are not always attracted to nursing home (NH) sectors because of limited exposure and lack of confidence. Innovative collaborative approaches are required to prepare NAs to care for seniors. METHODS: A 2009 collaboration between a NH group and a community college resulted in the Living Classroom (LC), a collaborative approach to integrated learning where NA students, college faculty, NH teams, residents, and families engage in a culture of learning. This approach situates the learner within the NH where knowledge, team dynamics, relationships, behaviours, and inter-professional (IP) practice are modelled. RESULTS: As of today, over 300 NA students have successfully completed this program. NA students indicate high satisfaction with the LC and have an increased intention to seek employment in NHs. Faculty, NH teams, residents, and families have increased positive beliefs towards educating students in a NH. CONCLUSION: The LC is an effective learning approach with a positive and high impact learning experience for all. The LC is instrumental in contributing to a capable workforce caring for seniors.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".