The impact of leadership style in team-based primary care – staff satisfaction and motivation
Bibliographic record
Abstract
BACKGROUND: Leadership styles, beliefs, and behaviours are an important and critical component to the delivery of quality care in any primary care organisation. The human resource crisis in health care has resulted in greater investments in team-based care; however, some leaders may not have experience working in team-based settings. AIM: To explore what leadership characteristics, styles, and behaviours were most conducive to employee satisfaction, motivation, and delivery of care in a team-based primary care setting. DESIGN & SETTING: A qualitative study involving 16 community health centre (CHC) staff from six CHCs across Ontario, Canada. METHOD: Thematic analysis of qualitative interviews using a framework based on transformational leadership (TL) theory. RESULTS: The following three themes emerged from our findings as having a noticeable impact on staff motivation, morale, delivery of care, and client outcomes: transparent and open communication; opportunities to collaborate in decision making; and staff recognition and appreciation. The results of our study indicate it is critical that leaders adopt leadership styles and approaches in which every team member is informed, heard, and appreciated. CONCLUSION: This study described the leadership styles and characteristics that lead to improved employee satisfaction, motivation, and morale in a team-based primary care setting, and the impact this could and does have on quality and delivery of care. Future research is needed to better understand the impact of leadership in a variety of roles within a team-based environment, specifically in a multidisciplinary setting.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".