The Concept of Decisional Control: Building the Base for Evidence‐Based Nursing Practice
Bibliographic record
Abstract
PURPOSES: The purposes of this article are to add clarification to the meaning of decisional control and provide a review of research related to the concept. DEFINITIONS AND CHARACTERISTICS OF DECISIONAL CONTROL: A summary of the definitions of decisional control is followed by a discussion of the characteristics of the concept. The concept of decisional control includes the ability or power to decide what will be one's involvement in health care decisions. REVIEW OF RESEARCH: Studies on decisional control have tended to center on inpatient, physician office, or clinic settings with a focus on the diagnosis of cancer in which multiple medical treatment decisions arise. Most of the research thus far has been performed in the United States and Canada related to medical treatment. At this point, it is not entirely clear how patient preferences for decisional control relate to health outcomes. MEASURING DECISIONAL CONTROL: An overview of the tools available to measure decisional control follows the review of the research. FUTURE DIRECTION: Future research needs to focus on the identification of the differences in decisional control by country, in differing cultural and ethnic groups within countries, and in various geographical areas of countries. The relationship of patient characteristics (e.g., age, gender, education, and income) to decisional control preferences is not clearly identified. A large gap in the research relates to how nurses can facilitate preferred decisional control to improve patient outcomes through evidence-based nursing practice.
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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 | 0.186 | 0.245 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.013 | 0.026 |
| 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".