Working in a ‘third space’: a closer look at the hybridity, identity and agency of nurse practitioners
Bibliographic record
Abstract
Nurse practitioners (NPs), as advanced practice nurses, have evolved over the years to become recognized as an important and growing trend in Canada and worldwide. In spite of sound evidence as to the effectiveness of NPs in primary care and other care settings, role implementation and integration continue to pose significant challenges. This article utilizes postcolonial theory, as articulated by Homi Bhabha, to examine and challenge traditional ideologies and structures that have shaped the development, implementation and integration of the NP role to this day. Specifically, we utilize Bhabha's concepts of third space, hybridity, identity and agency in order to further conceptualize the nurse practitioner role, to examine how the role challenges some of the inherent assumptions within the healthcare system and to explore how development of each to these concepts may prove useful in integration of nurse practitioners within the healthcare system. Our analysis casts light on the importance of a broader, power structure analysis and illustrates how colonial assumptions operating within our current healthcare system entrench, expand and re-invent, as well as mask the structures and practices that serve to impede nurse practitioner full integration and contributions. Suggestions are made for future analysis and research.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.055 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| 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".