Bibliographic record
Abstract
BACKGROUND: Health care organizations are considered complex systems that represent both formal leadership as well as more informal and shared leadership models. Implementing these models is essential for optimizing care and patient outcomes. The paramedic profession specifically, although considered informally, leads out of hospital patient care. PURPOSE: To date, few empirical studies investigate shared leadership in health care settings. In paramedicine specifically, studies of leadership are scarce, despite paramedics' essential role in leading on the front lines of care. Using an exemplar of paramedics, we examine what it means to informally lead on the front lines of patient care with the emphasis on paramedics responding out of hospital. METHODOLOGY: We employed a qualitative, semistructured interview methodology with 29 paramedics from a group of companies in central/eastern Canada to explore the conditions and practices surrounding shared leadership. FINDINGS: Paramedics argue that, despite their job title, they classify themselves as informal leaders who share the leadership role. More specifically, the paramedics discuss the precursors, practices, and structural conditions surrounding shared leadership within the realm of emergency medical services. They note that they often face out-of-hospital care without a formal manager, requiring them to collectively lead. The leader will shift in times of urgency, and this is contingent on their skills and competence. Furthermore, managers routinely called upon paramedics to lead in their absence. PRACTICE IMPLICATIONS: It is shown here that, although informally enacted, paramedics view leadership as a necessary competency for clinical practice. We argue that leadership development of paramedics must begin during their formal education and training as part of the core curriculum. As well, direct managers can promote an environment of shared leadership and encourage paramedics to practice leadership with quality of patient service in mind.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.111 | 0.034 |
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".