Educating registered nursing and healthcare assistant students in community-based supportive care of older adults: A mixed methods study
Bibliographic record
Abstract
BACKGROUND: Collaborative education that prepares nursing and healthcare assistant students in supportive care for older adults living at home with advanced chronic illness is an important innovation to prepare the nursing workforce to meet the needs of this growing population. OBJECTIVES: To explore whether a collaborative educational intervention could develop registered nursing and healthcare assistant students' capabilities in supportive care while enhancing care of clients with advanced chronic illness in the community. DESIGN: Mixed method study design. SETTING: A rural college in Canada. PARTICIPANTS: Twenty-one registered nursing and 21 healthcare assistant students completed the collaborative workshop. Eight registered nursing students and 13 healthcare assistant students completed an innovative clinical experience with fifteen clients living with advanced chronic illness. METHODS: Pre and post-test measures of self-perceived competence and knowledge in supportive care were collected at three time points. Semi-structured interviews were conducted to evaluate the innovative clinical placement. RESULTS: Application of Friedman's test indicated statistically significant changes on all self-perceived competence scores for RN and HCA students with two exceptions: the ethical and legal as well as personal and professional issues domains for HCA students. Application of Friedman's test to self-perceived knowledge scores showed statistically significant changes in all but one domain (interprofessional collaboration and communication) for RN students and all but three domains for HCA students (spiritual needs, ethical and legal issues, and inter-professional collaboration and communication). Not all gains were sustained until T-3. The innovative community placement was evaluated positively by clients and students. CONCLUSIONS: Collaborative education for nursing and healthcare assistant students can enhance self-perceived knowledge and competence in supportive care of adults with advanced chronic illness. An innovative clinical experience can maximize reciprocal learning while providing nursing services to a population that is not receiving home-based care.
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".