Linking research and practice: participation of nurses in research to influence policy
Bibliographic record
Abstract
In this article, the authors describe research conducted by the Pakistan Nursing Council (PNC) in Islamabad, Pakistan. The research was carried out through collaboration of two components of a large multicomponent Canadian International Development Agency (CIDA)-funded project--the Development of Women Health Professionals Program (DWHP)--with the Colleges of Nursing and the Pakistan Nursing Council. The research was guided by staff of the DWHP and performed by eight nurses undergoing a research course as part of a Diploma in Teaching Administration (DTA) at a postgraduate College of Nursing in the Northwest Frontier Province (NWFP), Pakistan. Research questions related to the collection and analysis of nursing workforce statistics were asked and partially answered, while students gained experience in conducting research. A description of the context in which the research was conducted is provided. Finally, results of the research and the potential benefits for influencing health workforce policy are discussed.
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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 | 0.483 | 0.406 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.054 |
| Scholarly communication | 0.036 | 0.028 |
| Open science | 0.004 | 0.040 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".