Breaching the professional social contract to drive system innovation: Nurse managers and the emergence of a new professional group
Bibliographic record
Abstract
The movement of healthcare professionals into hybrid manager positions is no longer seen as unusual within the course of a career. However, despite a continuing focus on the potential for hybrid managers to drive system level innovation, extant research suggests that potential is limited by the tensions inherent in the role, creating emotional turbulence and a lack of organizational influence. In this paper we explore these tensions as resulting from potentially unavoidable breaches of social contract, which all healthcare professionals becoming hybrid managers must navigate. Drawing on the case of nurse managers, we present findings from 120 h of ethnographic observation and 79 interviews conducted over three years. We identify three types of identity work in response to social contract breach: flipping between ignoring and separating expectations; reframing expectations; and decoupling expectations; and present a model exploring the outcomes and relationship between each of these responses over time. In doing so we give insight into the emergence of a new professional group we call ‘agents of innovation’, who hold the potential to drive system level innovation within healthcare. • Healthcare professional managers often encounter tensions in their role. • Tensions are created by conflicting social contracts and stereotypical expectations. • We identify three responses to breach of social contract with different outcomes. • We highlight the emergence of a new professional group to drive system innovation. • Our work is underpinned by assumptions from a Western healthcare context.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".