Transformative Learning and Research Utilization in Nursing Practice: A Missing Link?
Bibliographic record
Abstract
BACKGROUND: Poor or inconsistent research utilization into clinical practice is a recurrent theme across study contexts, rendering leaders disillusioned with how best to foster the uptake of research into nursing practice. This makes it imperative to look to new approaches. Research utilization involves a learning process engaging attitudes, beliefs, and behaviors; yet, this is often overlooked in approaches and models used to facilitate research use. This oversight may offer some explanation to the limited progress in research utilization to date. Transformation Theory offers an explanatory theory and specific strategies (critical reflection and critical discourse) to explore attitudes, beliefs, and behaviors so that they are understood, validated, and can better guide actions. AIM: The purpose of this article was to explore what Transformation Theory can contribute to research utilization initiatives in nursing practice. APPROACH: Transformation Theory and transformative learning strategies are discussed and critically analyzed in consideration of their potential roles in fostering research utilization in clinical nursing practice. ISSUES AND CONCLUSIONS: (1) Research utilization is a learning process that involves knowledge, skills, feelings, attitudes, and beliefs. (2) Transformative learning strategies of critical reflection and discourse can facilitate insight into experiences, finding shared meanings among groups of people, and understanding/validating beliefs, attitudes, and feelings so they can more consciously guide future actions. This dimension is frequently neglected in research utilization efforts. (3) In combination with research utilization theories, Transformation Theory may be a missing link to make research utilization initiatives more effective in rendering and sustaining nursing practice change, thus enhancing client care and well-being. (4) Research and further consideration are both warranted and needed.
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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 | 0.098 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.068 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.008 |
| 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; 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".