Can Workshops Provide a Way to Enhance Patient/Client Centered Collaborative Teams?: Evidence of Outcomes from TEAMc Online Facilitator Training and Team Workshops
Bibliographic record
Abstract
The movement towards collaborative interprofessional teamwork for improving patient care has sometimes been impeded by health providers who have a desire to work together, but are unsure how to move towards such models of care delivery. The situation can be complicated by some reluctance on the part of health care institutions to release staff from normal duties to participate in team building training. The purpose of this study was to report on a collaborative team building process supported by the hospital administration in northern Ontario, Canada, and to provide evaluation results for the Toolkit for Enhancing and Maintaining Team Collaboration (TEAMc) using measurements before the start, at the end of the workshop series and at eight months post-series. Participants were from two teams (Acute Care and Rehabilitation) in a northern Ontario, Canada, hospital. TEAMc was comprised of six, 3-hour workshops offered over six months in 2014/15. A total of 77 health providers completed the pre-intervention Interprofessional Socialization & Valuing Scale (ISVS) and the Assessment of Interprofessional Team Collaboration Scale (AITCS), 50 health providers completed the post-intervention instruments and 32 and at the eight month follow-up. The study found that TEAMc can result in changes in team members’ socialization towards wanting to participate in interprofessional teams and in the team’s ability to emulate interprofessional client-centered collaborative practice. The greatest learning gained by participants was around their role clarification and understanding of each other’s roles and expertise, as well as developing their capacity to use a process to resolve interprofessional conflicts.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".