Environmental scan of interprofessional collaborative practice initiatives
Bibliographic record
Abstract
Interprofessional collaboration in health care is high on the policy agenda in Canada. There is evidence that governments, academic institutions, regulatory bodies and health services are developing directions, policies and strategies with collaboration in mind. The Ottawa Hospital (TOH) received governmental funding to implement The Ottawa Hospital Inter-Professional Model of Patient Care.Prior to implementing our model, we conducted an environmental scan to identify initiatives related to interprofessional collaboration in clinical settings. A historical method was used to understand the chronological development of interprofessional collaboration within the health field over the last 10 years. Critical browsing was used to search, select and summarize information found on the web. Fifty two documents were critically reviewed; 27 documents were retained for further analysis and inclusion.The information was analyzed according to three main parameters: source, summary and relevance to our project. The five broad themes identified are: promotion, networking, evidence, resources and linkage between interprofessional education and care. This seems an accurate reflection of the current state of this area; there is active promotion and networking, concrete frameworks and funds but few published results regarding the efficacy of implementing IPC in health care organizations. As experience with the approach accumulates, evidence should grow
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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.043 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".