Primary care practice characteristics associated with team functioning in primary care settings in Canada: A practice-based cross-sectional survey
Bibliographic record
Abstract
Team-based care is recognized as a foundational building block of high-performing primary care. The purpose of this study was to identify primary care practice characteristics associated with team functioning and examine whether there is relationship between team composition or size and team functioning. We sought to answer the following research questions: (1) are primary care practice characteristics associated with team functioning; and (2) does team composition or size influence team functioning. This cross-sectional correlational study was conducted in Fraser East, British Columbia, Eastern Ontario Health Unit, Ontario and Central Zone, Nova Scotia in Canada. Data were collected from primary care practices using an organization survey and the Team Climate Inventory (TCI) as a measure team functioning. The independent variables of interest were: physicians’ payment model, internal clinic meetings to discuss clinical issues, care coordination through informal and ad hoc exchange, care coordination through electronic medical records and sharing clinic mission, values and objectives among health professionals. Potentially confounding variables were as follows: team size, composition, and practice panel size. A total of 63 practices were included in these analyses. The overall mean score of team climate was 73 (SD: 10.75) out of 100. Regression analyses showed that care coordination through human interaction and sharing the practice’s mission, values, and objectives among health professionals were positively associated with higher functioning teams. Care coordination through electronic medical records and larger team size were negatively associated with team climate. This study provides baseline data on what practice characteristics are associated with highly functioning teams in Canada.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".