Observed Interprofessional Collaboration (OIPC) During Interdisciplinary Team Meetings: Development and Validation of a Tool in a Rehabilitation Setting
Bibliographic record
Abstract
Background: Despite all the efforts made in the past few years, interprofessional collaboration (IPC) in clinical settings is not always optimal. In addition, there are only a few instruments that healthcare managers and practitioners can use to evaluate the quality of IPC practice. Therefore, we developed an observationbased tool to evaluate IPC interactional factors occurring during interdisciplinary team meetings, and we examined the initial validation of the tool in a rehabilitation setting.Methods and Findings: The items were developed and pre-tested iteratively by construct experts (N = 7) and non-experts (N = 4). Interrater reliability was determined between two observers, following the analysis of 30 video recordings of meetings in two rehabilitation centres involving a total of 152 participants. An observation grid (OIPC) consisting of 20 items that can be answered on a threepoint scale and demonstrating acceptable interrater reliability was developed.Conclusions: The OIPC is a tool aimed at evaluating IPC interactional factors during interdisciplinary meetings based on team performance rather than individual behaviours. It can be useful for healthcare managers and practitioners who want to evaluate the quality of IPC practices.
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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 | 0.036 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".