eLearning, Knowledge Brokering, and Nursing
Bibliographic record
Abstract
Interprofessional collaboration is vital to the delivery of quality care in long-term care settings; however, caregivers in long-term care face barriers to participating in training programs to improve collaborative practices. Consequently, eLearning can be used to create an environment that combines convenient, individual learning with collaborative experiential learning. Findings of this study revealed that learners enjoyed the flexibility of the Working Together learning resource. They acquired new knowledge and skills that they were able to use in their practice setting to achieve higher levels of collaborative practice. Nurses were identified as team leaders because of their pivotal role in the long-term care home and collaboration with all patient care providers. Nurses are ideal as knowledge brokers for the collaborative practice team. Quantitative findings showed no change in learner's attitudes regarding collaborative practice; however, interviews provided examples of positive changes experienced. Face-to-face collaboration was found to be a challenge, and changes to organizations, systems, and technology need to be made to facilitate this process. The Working Together learning resource is an important first step toward strengthening collaboration in long-term care, and the pilot implementation provides insights that further our understanding of both interprofessional collaboration and effective eLearning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".