Influence of a quality improvement learning collaborative program on team functioning in primary healthcare.
Bibliographic record
Abstract
Quality improvement (QI) programs are frequently implemented to support primary healthcare (PHC) team development and to improve care outcomes. In Ontario, Canada, the Quality Improvement and Innovation Partnership (QIIP) offered a learning collaborative (LC) program to support the development of interdisciplinary team function and improve chronic disease management, disease prevention, and access to care. A qualitative study using a phenomenological approach was conducted as part of a mixed-method evaluation to explore the influence of the program on team functioning in participating PHC teams. A purposive sampling strategy was used to identify PHC teams (n = 10), from which participants of different professional roles were selected through a purposeful recruitment process to reflect maximum variation of team roles. Additionally, QI coaches working with the interview participants and the LC administrators were also interviewed. Data were collected through semistructured telephone interviews that were audiotaped and transcribed verbatim. Thematic analysis was conducted through an iterative and interpretive approach. The shared experience of participating in the program appeared to improve team functioning. Participants described increased trust and respect for each other's clinical and administrative roles and were inspired by learning about different approaches to interdisciplinary care. This appeared to enhance collegial relationships, collapse professional silos, improve communication, and increase interdisciplinary collaboration. Teamwork involves more than just physically grouping healthcare providers from multiple disciplines and mandating them to work together. The LC program provided opportunities for participants to learn how to work collaboratively, and participation in the LC program appeared to enhance team functioning.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".