Impact of culture on commitment, satisfaction, and extra‐role behaviors among Canadian ER physicians
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to explore the impact of hospital emergency department culture on the job satisfaction, patient commitment, and extra-role performance of Canadian emergency physicians. The conceptual model related four cultural archetypes from the competing valued model to the three outcome variables. DESIGN/METHODOLOGY/APPROACH: In total, 428 Canadian emergency physicians responded to a national survey. The conceptual model was tested via structural equation modeling via LISREL 8. FINDINGS: Culture had a relatively weak impact on the outcomes. Human resources culture related positively to job satisfaction while bureaucratic culture related positively to patient commitment. Patient commitment, but not job satisfaction strongly and positively related to extra-role behavior. A direct relationship between entrepreneurial culture and extra-role behavior emerged from an extended analysis. PRACTICAL IMPLICATIONS: Organizational culture seems to have more distal relationships with outcome variables and its influence is likely to be mediated by more proximal workplace variables. ORIGINALITY/VALUE: Of value by showing that a key modern leadership challenge is to create the kind of work culture that can become a source of competitive advantage through generating particular organizational outcomes valued by stakeholders.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".