Development of a Measure to Delineate the Clinical Trials Nursing Role
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To identify the significant dimensions of the clinical trials nursing role and to construct a reliable and valid survey instrument to reflect these dimensions. DESIGN: Methodologic survey. SETTING/SAMPLE: The judge panel consisted of six national nurse experts. The focus group sample was comprised of 24 clinical research nurses from the West, Northeast, and Great Lakes regions of the United States and five research nurses from Canada. The sample for instrument testing consisted of 40 oncology clinical research nurses from the Southeast. METHODS: Several strategies were used to develop the Clinical Trials Nursing Questionnaire (CTNQ): literature review, conceptualization of the subscales, development of items for each subscale, development of the tool, expert judge panel evaluation, focus group testing, administration of the tool, and psychometric analysis of the results. MAIN RESEARCH VARIABLES: Frequency and importance of clinical trials nursing activities. FINDINGS: Content validity was established at 0.95. The alpha reliability coefficient was 0.92 for the frequency scale and 0.95 for the importance scale. A two-week test-retest reliability of 0.88 was obtained for the frequency scale and 0.92 for the importance scale. The final CTNQ contained 12 sections with 154 items. CONCLUSIONS: The CTNQ has acceptable content validity, internal consistency, and stability reliability. This instrument is promising for the assessment of the research nurse role, and its use in further research is appropriate. IMPLICATIONS FOR NURSING: A valid and reliable measure can be used to delineate the subspecialty of clinical trials nursing, thus providing a better understanding of how nursing professionals contribute to the cancer research enterprise.
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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 | 0.050 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".