Interprofessional collaboration in health care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Improved health care collaboration has been cited as a key strategy for health care reform.1,2 Collaboration in health care has been shown to improve patient outcomes such as reducing preventable adverse drug reactions,3,4 decreasing morbidity and mortality rates5,6 and optimizing medication dosages.7 Teamwork has also been shown to provide benefits to health care providers, including reducing extra work4 and increasing job satisfaction.8 As a fourth-year pharmacy student at the University of Saskatchewan (BB), I have noticed this shift becoming increasingly evident in our education, with the incorporation of interprofessional-based learning activities and relocation to a recently built health sciences building. Having played competitive hockey for about 15 years, I have been on both highly successful teams that went on to win championships, as well as teams that were unable to function effectively. What was it about these teams that contributed to our successes or failures? Moreover, can these lessons be extrapolated to health care teams? Characteristics have been identified in both sports and health care that may influence team success.2,9 Examples include accountability, communication, leadership, discipline, coordination, having a clear purpose and having a strategy in place. While a cooking recipe may consist of many ingredients (some perhaps to add flavour; others for consistency), a few ingredients will always remain essential. Reflecting on my experiences as an athlete and as a pharmacy student, 5 key ingredients seem necessary for success in a collaborative team (Table 1). Table 1 Five essential ingredients for team success
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it