Difficulties Encountered in Collaborative Care: Logistics Trumps Desire
Bibliographic record
Abstract
INTRODUCTION: This study examines the development of collaborative relationships between family physicians (FPs) and Anticipatory And Preventative Team Care (APTCare) team members providing care to medically complex patients who have been identified as at-risk for negative health outcomes. METHODS: We undertook a qualitative study of a primary health care intervention in a family practice. Interviews were held with FPs and ATPCare intervention nurse practitioners (NPs) and pharmacists. Focus groups were conducted and a survey was administered to participating FPs, NPs, and pharmacists. NPs and pharmacists maintained a log recording their tasks and moments of collaboration. RESULTS: Scheduling demands rendered face-to-face collaboration difficult, leaving the team to rely on technological tools to keep in touch. Limited space meant the APTCare team had to work out of a downstairs office, limiting informal interactions with the practitioners on the main level. CONCLUSIONS: We demonstrate that the difficulties inherent in collaborative care are independent of the patient population being cared for. Regardless of the patient population and sector of health care, developing collaborative relationships and learning to work collaboratively is difficult and takes time. What many of these teams need is ongoing support and education about how to make these collaborative care practices work.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".