It starts with a strong foundation: constructing collaborative interprofessional teams in primary health care
Bibliographic record
Abstract
The purpose of this qualitative study was to explore how team members experience and enact interprofessional teamwork in primary health care (PHC). Fifty-three participants (from eight teams), members of the Association of Family Health Teams of Ontario (AFHTO), were interviewed; interviews were audiotaped and transcribed verbatim. The data analyses used an iterative process with individual and team analysis. Findings revealed components that comprise the foundation and pillars of collaborative interprofessional teamwork in PHC. First, participants described a shared philosophsy of teamwork with six elements: values, vision, and mission; collaboration; communication; trust; respect and team members that ‘fit.’ Second, findings revealed three ‘pillars.’ The first pillar, leadership, included the elements of specific leadership attributes, such as leaders encouraging teamwork, mitigating conflict, and facilitating change. In the second pillar, participants described three elements of team building: formal and informal team building activities plus how these activities benefited both the team and patient care. The last pillar, optimizing scope of practice, included the elements of recognizing, appreciating, utilizing, and expanding team members’ scope of practice. While each component and their concomitant elements can be enacted individually, collectively applying all elements produces collaborative interprofessional teamwork in primary health care.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".