Advanced Practice Nursesâ Perceptions of the Lived Experience of Power
Bibliographic record
Abstract
“Power” is a concept that has been discussed by nurse scholars and leaders within the nursing literature. The literature surrounding power concurs that power is necessary within the practice of nursing so that nurses are able to support patients and move the profession of nursing forward. There is a scarcity of research, however, regarding nurses’ perception of power within their own practices. Advanced practice nurses (APNs) are in positions in which they apply graduate education, specialized knowledge, and expertise to improve health care outcomes. Therefore, a qualitative study using an interpretive hermeneutic phenomenological approach was undertaken to discover APNs’ lived experience of power within their practices. In-depth, tape-recorded interviews were conducted with eight APNs from a large tertiary care facility. All of the participants agreed to a follow-up interview to review the summary of the study results. van Manen’s (1990) approach was used to analyze the data by subjecting the transcripts to a thematic analysis and reflective process. The overarching theme of the interviews is “building to make a difference” and the APNs’ perceived that this happened by “building on,” building with,” and “building for.” The APNs built on their knowledge and expertise, built with others in relationships and built for the capacity to make a difference. Power was a part of the everyday practices of these APNs and was described as “soft power,” a power that they shared to bring about change for the better. This shared power was reflected back on them resulting in increased power within their practices, a process described by the APNs as power creep.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| 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".