Building Nurses' Capacity in Community Health Services
Bibliographic record
Abstract
This paper describes core processes, components, and insights gained from a research internship offered through the University of Ottawa, Canada. The growing demand for high quality nursing research requires the development and implementation of strategies for enhanced research capacity. A three-month intensive internship was developed as a main feature of a nursing chair held by the first author. The internship was deliberately structured around core processes of providing individual and group mentoring, creating opportunities for experiential education, and strengthening networks with researchers and decision-makers in health services and policy research. Building and sustaining individual research capacity was supported with strategies to address system challenges. If nurses are going to make their voices heard and increase their contributions to novel health service delivery approaches, building research capacity will be a core element. The internship may be a useful prototype for the development of initiatives to build research capacity in other settings.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".