Stepping up to Interprofessional Practice: A Health Department Promotes Interdisciplinary Learning
Bibliographic record
Abstract
This paper accepts that complex health and social care requires collaboration and team work. For any team to succeed, rehearsing, practicing or training is essential. In most areas of life team training occurs before the game and continues between games. In the health system there is some debate about whether interprofessional learning (IPL) needs to occur before licensure (registration), or can be left until after licensure. However, there is general agreement that IPL or team training must happen if health teams are to work successfully. We argue that Australia lags behind the UK, US, Canada and other similar countries in implementing IPL because state, territory and the Australian government haven't provided leadership in the field (Productivity Commission 2005, p. 46). In the measured terms of the Productivity Commission's Position Paper on the health workforce 'an active approach' is needed that identifies the collaborative outcomes wanted and the mechanisms to achieve them. If we in Australia are to have a Southampton, then governmental action, at both the state and national level, to plan for, fund and support IPL will be needed.
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 | 0.016 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".