Patient Satisfaction as an Endoscopy Training Evaluation Tool for Nurses Performing Flexible Sigmoidoscopy
Bibliographic record
Abstract
Purpose: A program to train registered nurses to perform flexible sigmoidoscopy (FS) has been developed in Ontario, Canada to create increased endoscopy capacity to screen for colorectal cancer. The nurses initially undertook simulator training and they have begun performing procedures on patients. This represents a new role for nurses in Ontario. Objectives: To develop standardized evaluation tools to monitor nurses' performance of FS on patients. Methods: Evaluation of performance on patients was conducted by checklists and global assessments completed by expert observers and by patients completing a patient satisfaction survey. We modified the Patient Satisfaction Questionnaire III (PSQ-III) to include five domains: interpersonal aspects, communication, technical quality, time spent with provider, and general satisfaction. Results: Five nurse trainees have performed at least ten procedures each. Figure 1 shows the general satisfaction scores of patients seen by two different trainees. It demonstrates that with increasing experience, an improving profile is seen for one trainee (Figure 1a) but a deteriorating profile is seen for another (Figure 1b). These trends are supported by the evaluations obtained by checklists and global assessments. (Data not shown.)FigureConclusions: These preliminary data show that the general satisfaction of patients improves with increasing skill of the nurse trainee and they may help differentiate between endoscopists with good skill level and those with poor skill level. The trainees continue to see patients and further data will be collected and evaluated. [figure 1][figure 2]Figure
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".