Process of Seeking Connectivity: Social Relations of Power between Staff Nurses and Nurse Managers
Bibliographic record
Abstract
This study explored the process of how power is exercised in nurse-manager relationships in the hospital setting, to better understand what fosters and constrains staff nurse empowerment. Semi-structured interviews and participant observations were conducted with 26 participants in a hospital in Western Canada. Seeking connectivity was the basic social process in which nurses strive to connect with their manager to create a workable partnership in the provision of high-quality patient care while responding to the demands of the organizational context. The overarching finding was that the manager plays a critical role in modifying the work environment for nurses and, as such, nurses seek connection with their manager. Findings revealed two patterns within the process of seeking connectivity: (a) in the absence of a meaningful engagement with the manager, power was held over nurses through institutional patterns of behaviour and practices, and nurses employed a variety of resistance strategies; (b) when managers provided guidance and engaged nurses as co-collaborators, power was shared and nurses were able to influence patient outcomes positively. The results of this study support Laschinger's program of research on nurse empowerment from an organizational perspective, and advance nurse empowerment from a critical perspective.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".