Qualitative Research in an International Research Program: Maintaining Momentum while Building Capacity in Nurses
Bibliographic record
Abstract
Nurses are knowledgeable about issues that affect quality and equity of care and are well qualified to inform policy, yet their expertise is seldom acknowledged and their input infrequently invited. In 2007, a large multidisciplinary team of researchers and decision-makers from Canada and five low- and middle-income countries (Barbados, Jamaica, Uganda, Kenya, and South Africa) received funding to implement a participatory action research (PAR) program entitled “Strengthening Nurses' Capacity for HIV Policy Development in sub-Saharan Africa and the Caribbean.” The goal of the research program was to explore and promote nurses' involvement in HIV policy development and to improve nursing practice in countries with a high HIV disease burden. A core element of the PAR program was the enhancement of the research capacity, and particularly qualitative capacity, of nurses through the use of mentorship, role-modeling, and the enhancement of institutional support. In this article we: (a) describe the PAR program and research team; (b) situate the research program by discussing attitudes to qualitative research in the study countries; (c) highlight the incremental formal and informal qualitative research capacity building initiatives undertaken as part of this PAR program; (d) describe the approaches used to maintain rigor while implementing a complex research program; and (e) identify strategies to ensure that capacity building was locally-owned. We conclude with a discussion of challenges and opportunities and provide an informal analysis of the research capacity that was developed within our international team using a PAR approach.
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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 | 0.412 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.042 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.006 | 0.038 |
| Research integrity | 0.005 | 0.017 |
| 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".