The metamorphosis of a collaborative team: from creation to operation
Bibliographic record
Abstract
This paper reports on the process of developing a community-based interprofessional team to provide diabetes related foot ulcer care. A new interprofessional team was formed in a local community, and the process of building a successful team was examined by the adoption of an exploratory qualitative case study approach that gathered a series of one-on-one interviews with participants at three points in time - prior to the team's formation, two months into the team's operation, and finally seven months later - shortly before the team and its clinic closed. Interviews were also conducted with a small sample of the team's patients. The factors linked to the successes and challenges of building a care team in a community setting are explored. Informants highlighted the value of regular team meetings, role clarity, and a commitment to patient-centered care. However effective collaboration was not sufficient to maintain the team in the face of poor institutional and government support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".