How can a social capital framework guide managers to develop positive nurse relationships and patient outcomes?
Bibliographic record
Abstract
AIM: To examine how social capital could be a mediating factor through which managers' leadership positively influences relationships with nurses and quality patient outcomes. BACKGROUND: The relationship between leadership, what managers do and optimal outcomes for patients are well established. What is not yet clear is an understanding about specific mechanisms by which managers' leadership builds social capital to foster cohesive team relationships and quality patient outcomes. KEY ISSUES: Conceptual links are drawn between human capital and leadership styles of managers. Social capital is introduced and contextualized through exemplars from a Canadian study. Exemplars illustrate how the presence or absence of social capital influenced nurses' productivity to deliver quality patient care. CONCLUSIONS: Nurse researchers could use the Social Capital Framework (SCF) to examine the mediating role of social capital in relationships between managers and nurses. These findings could inform managers' strategies to foster positive networks and norms between nurses to deliver quality patient care. IMPLICATIONS FOR NURSING MANAGEMENT: Leadership that uses a framework of social capital will enhance team relationships between nurses. Enhanced cohesion will have a positive impact on patient outcomes.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".