Communication, Respect, and Leadership: Interprofessional Collaboration in Hospitals of Rural Ontario
Bibliographic record
Abstract
PURPOSE: Health care professionals are expected to work collaboratively across diverse settings. In rural hospitals, these professionals face different challenges from their urban colleagues; however, little is known about interprofessional practice in these settings. METHODS: Eleven health care professionals from 2 rural interprofessional teams were interviewed about collaborative practice. The data were analyzed using a constant comparative method. RESULTS: Common themes included communication, respect, leadership, benefits of interprofessional teams, and the assets and challenges of working in small or rural hospitals. Differences between the cases were apparent in how the members conceptualized their teams, models of which were then compared with an "Ideal Interprofessional Team". CONCLUSIONS: These results suggest that many experienced health care professionals function well in interprofessional teams; yet, they did not likely receive much education about interprofessional practice in their training. Providing interprofessional education to new practitioners may help them to establish this approach early in their careers and build on it with additional experience. Finally, these findings can be applied to address concerns that have arisen from other reports by exploring innovative ways to attract health professionals to communities in rural, remote, and northern areas, as there is a constant need for dietitians and other health care professionals in these practice settings.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".