Using a mixed-methods design to examine nurse practitioner integration in British Columbia
Bibliographic record
Abstract
AIM: To discuss and provide examples of how mixed-methods research was used to evaluate the integration of nurse practitioners (NPs) into a Canadian province. BACKGROUND: Legislation enabling NPs to practise in British Columbia (BC) was enacted in 2005. This research evaluated the integration of NPs and their effect on the BC healthcare system. DATA SOURCES: Data were collected using surveys, focus groups, participant interviews and case studies over three years. REVIEW METHODS: Data sources and methods were triangulated to determine how the findings addressed the research questions. DISCUSSION: The challenges and benefits of using the multiphase design are highlighted in the paper. CONCLUSION: The multiphase mixed-methods research design was selected because of its applicability to evaluation research. The design proved to be robust and flexible in answering research questions. IMPLICATIONS FOR PRACTICE/RESEARCH: As sub-studies within the multiphase design are often published separately, it can be difficult for researchers to find examples. This paper highlights ways that a multiphase mixed-methods design can be conducted for researchers unfamiliar with the process.
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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 | 0.056 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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".