The Effect of Early Education on Patient Anxiety While Waiting for Elective Cardiac Catheterization
Bibliographic record
Abstract
BACKGROUND: A supply-demand mismatch with respect to cardiac catheterization (CATH) often results in patients experiencing waiting times that vary from a few weeks to several months. Long delays can impose both physical and psychological distress for patients. PURPOSE: The purpose of this study was to examine the effect of a psychoeducational nursing intervention at the beginning of the waiting period on patient anxiety during the waiting time for elective CATH. METHODS: This was a 2-group randomized controlled trial. Intervention patients received a nurse-delivered, detailed information/education session within 2 weeks of being placed on the waiting list for elective CATH. Control group patients received usual care. RESULTS: The mean waiting time for CATH was 13.4+/-7.2 weeks, which did not differ between groups (P=0.509). Anxiety increased in both groups over the waiting time (P=0.028). Health-related quality of life deteriorated over the waiting time in both groups (P<0.05). On a visual analogue scale, there was a significant difference (P=0.002) between the intervention (4.0+/-2.7) and control (5.2+/-3.0) groups in self-reported anxiety 2 weeks prior to CATH. CONCLUSIONS: The waiting period prior to elective CATH has a negative impact on patients' perceived anxiety and quality of life and a simple intervention, provided at the beginning of the waiting period, may positively affect the experience of waiting.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".