Measuring teamwork in primary care: Triangulation of qualitative and quantitative data.
Bibliographic record
Abstract
This article describes the triangulation of qualitative dimensions, reflecting high functioning teams, with the results of standardized teamwork measures. The study used a mixed methods design using qualitative and quantitative approaches to assess teamwork in 19 Family Health Teams in Ontario, Canada. This article describes dimensions from the qualitative phase using grounded theory to explore the issues and challenges to teamwork. Two quantitative measures were used in the study, the Team Climate Inventory (TCI) and the Providing Effective Resources and Knowledge (PERK) scale. For the triangulation analysis, the mean scores of these measures were compared with the qualitatively derived ratings for the dimensions. The final sample for the qualitative component was 107 participants. The qualitative analysis identified 9 dimensions related to high team functioning such as common philosophy, scope of practice, conflict resolution, change management, leadership, and team evolution. From these dimensions, teams were categorized numerically as high, moderate, or low functioning. Three hundred seventeen team members completed the survey measures. Mean site scores for the TCI and PERK were 3.87 and 3.88, respectively (of 5). The TCI was associated will all dimensions except for team location, space allocation, and executive director leadership. The PERK was associated with all dimensions except team location. Data triangulation provided qualitative and quantitative evidence of what constitutes teamwork. Leadership was pivotal in forging a common philosophy and encouraging team collaboration. Teams used conflict resolution strategies and adapted to the changes they encountered. These dimensions advanced the team's evolution toward a high functioning team.
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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 | 0.126 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".