Using a mixed-methods design to examine nurse practitioner integration in British Columbia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it