How case-study research can help to explain implementation of the nurse practitioner role
Bibliographic record
Abstract
AIM: To discuss how case-study research was undertaken to explain the implementation of the nurse practitioner role in a Canadian province. BACKGROUND: In Canada, the nurse practitioner role was only recently introduced and one of the last provinces to implement it was British Columbia. At this time, no studies of the role's implementation in the province had been published and, although nurses refer to case studies more frequently in their research, the literature lacks concise explanations of the methodologies involved in creating them. DATA SOURCES: A case study of the implementation of the nurse practitioner role, including participant interviews and document review. RESEARCH METHODOLOGY: The development of an explanatory, single case study with embedded units of analysis in line with Yin's (2009) approach to case-study research. DISCUSSION: This article provides an overview of case-study research methodology and examples from a case study undertaken by the author. CONCLUSION: The use of case studies provides nurse researchers with opportunities to engage with phenomena of interest in their settings and so is suited to the complex nature of nursing practice. IMPLICATIONS FOR PRACTICE OR RESEARCH: Case-study research enables researchers to study areas of interest thoroughly and in the context in which they occur.
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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 | 0.092 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".