The art and science of teamwork: Enacting a transdisciplinary approach in work rehabilitation
Bibliographic record
Abstract
Teamwork, collaboration and interprofessional care are becoming the new standard in health care, and service delivery in work practice is no exception. Most rehabilitation professionals believe that they intuitively know how to work collaboratively with others such as workers, employers, insurers and other professionals. However, little information is available that can assist rehabilitation professionals in enacting authentic transdisciplinary approaches in work practice contexts. A qualitative study was designed using a grounded theory approach, comprised of observations and interviews, to understand the social processes among team members in enacting a transdisciplinary approach in a work rehabilitation clinic. Findings suggest that team members consciously attended to a team approach through nurturing consensus, nurturing professional synergy, and nurturing a learning culture. These processes enabled this team to work in concert with clients who had chronic disabilities in achieving solution focused goals for returning to work and improving functioning. Implications for achieving greater collaborative synergies among stakeholders in return to work settings and in the training of new rehabilitation professionals are explored.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".