Back to Back: Having interprofessional education during the undergraduate years is essential for building teamwork skills in general practice: Yes
Bibliographic record
Abstract
A team is a small number of people with complementary skills who are committed to a common purpose, performance goals, and approach for which they hold themselves mutually accountable'.1 Interprofessional teamwork in general practice leads to better utilisation of skill sets, enhances workflow, economic sustainability and improves patient satisfaction, with a common aim to reduce duplication, delay, discontinuity and mistakes.The need for collaborative teamwork, first promulgated in the Alma-Ata Declaration, endorsed in the Ottawa Charter, now features in New Zealand's 'Better, Sooner, More Convenient' government policy.Teamwork is needed when 'no one health care provider can meet all the complex needs of a patient and his/her family' 2 and this is particularly the case in long-term condition care.Modern general practice is uniquely placed to provide the 'medical/clinical home' for rapidly increasing numbers of such complex patients.However, coordination of care is multifaceted and primary care teams are evolving at a variable rate to embrace the 'deep' teamwork necessary to provide best patient care.Deep teamwork
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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.041 | 0.014 |
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".