An online interprofessional learning resource for physicians, pharmacists, nurse practitioners, and nurses in long-term care: Benefits, barriers, and lessons learned
Bibliographic record
Abstract
The importance of collaborative practice in health care has been emphasized.1,21, 2 There is a critical need for convenient and flexible education opportunities that support the development of collaborative practice skills among the health care workforce. Consequently, the purpose of this project was to create and evaluate an online learning resource for physicians, nurses, nurse practitioners, and pharmacists working in long-term care that provided practitioners with the skills, knowledge, and motivation necessary to enhance their ability to act as an interprofessional team while providing clinical care. The Demand-Driven Learning Model 3 was used to guide the project. Findings revealed learners enjoyed the programme and acquired new skills and knowledge relating to collaborative practice that they transferred to their workplace resulting in higher levels of collaborative practice. The data did not reveal significant changes in the learners' attitudes towards collaborative practice; perhaps because the participants were early adopters and already had positive attitudes. Requests to change organizational structure to enhance collaborative practice were minimal, as was the impact of the resource on resident care. Given the short time frame from completion of the learning resource to the evaluation, this is perhaps not surprising as it is reasonable to expect that these types of changes will take time to take effect within the organization. Follow-ups at a later date are suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".