Teams of rural physicians matter
Bibliographic record
Abstract
<h3>Objective</h3> To examine how rural physician team effectiveness predicts outcomes of team performance, team commitment, and intentions to stay. <h3>Design</h3> Surveys measuring team climate, team efficacy, and team performance were sent to rural physician team members. Surveys measuring team performance were sent to external observers in supervisory positions. <h3>Setting</h3> Northern Ontario communities. <h3>Participants</h3> Rural physicians and external observers, the latter including hospital chief executive officers, family health team executive directors, and clinic managers. <h3>Main outcomes measures</h3> Total scale scores were generated using mean substitution. Cronbach α was used to assess internal consistencies of team member—level measures. Team-level measures were created by averaging the responses across team members, and intraclass correlation coefficients for each scale of each team of 2 or more members were calculated to yield a measure of rating consistency. A <i>t</i> test was used to assess the possible difference between team performance ratings by team members and external observers. Team-level relationships within the team effectiveness model were assessed using mediated regression, and generalized estimating equations were used to assess the relationships in the model between team-level (team efficacy) and individual-level (affective team commitment and intentions to stay) variables to address the nonindependence of these data. <h3>Results</h3> Overall, 70 rural physicians from 26 Rural and Northern Physician Group Agreement communities with 2 or more physicians and 25 external observers from 19 of the 26 Rural and Northern Physician Group Agreement communities participated in the study. The findings showed that team climate (composed of decision making, communication, and conflict resolution measures) positively predicted team efficacy, which in turn positively predicted team performance. This fully mediated set of relationships held whether team performance was rated by the physicians themselves or by the external observers. Team efficacy significantly predicted affective team commitment (<i>b</i> value=0.69, standard error=0.08, Wald =13.89, <i>P</i><.001) in the first analysis and intentions to stay (<i>b</i> value=0.34, standard error=0.15; Wald =5.42, <i>P</i>=.020) in the second analysis. However, when the other variables impacting physician retention were added to the model in predicting intentions to stay, team efficacy did not predict it above and beyond these additional predictors. <h3>Conclusion</h3> The findings support initiatives that attempt to enhance physician team effectiveness in rural physician teams by influencing team decision making, communication, and conflict resolution to improve team performance, physician attitudes, and commitment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".