Patient‐reported experiences of consultation with an advanced nurse practitioner: Factor structure and reliability analysis of the patient enablement and satisfaction survey
Bibliographic record
Abstract
AIM: The aim was to analyse the psychometric properties of a patient-reported-experience measure, the Patient Enablement and Satisfaction Survey (PESS), when used to evaluate the care provided by Advanced Nurse Practitioners (ANPs) in terms of factor structure and internal consistency. The PESS is a 20-item, patient-completed data collection tool that was originally developed to measure patient experience and enablement following consultation with nurses in general practice. DESIGN: Cross-sectional survey; validity and reliability analysis. METHODS: The sample in this study consisted of 178 patients who consulted with 26 ANPs working in four different specialities. Data were collected between June and December 2019. An exploratory factor analysis of the PESS was conducted to determine convergent validity which was supported by parallel analysis and the traditional Kaiser criterion. The internal consistency of individual PESS items was determined via Cronbach's alpha, McDonald's omega, the Average Variance Extracted tests and item-subscale/total score correlations. RESULTS: A three-factor structure (PESS-ANP) was found through exploratory factor analysis and this was supported by parallel analysis, the traditional Kaiser criterion and the percentage of variance explained criterion. A high degree of internal consistency was reported across all factors. One question was omitted from the analysis ('Overall Satisfaction') following the identification of problematic cross-loadings. The three factor solution was identified as: patient satisfaction, quality of care provision and patient enablement. CONCLUSION: The findings of this study propose a three-factor model that is sufficiently reliable for analysing the experience and enablement of patients following consultation with an ANP. IMPACT: Increasingly, patient-reported experience measures are being used to evaluate patients' experience of receiving care from a healthcare professional. The PESS was identified to be reliable in evaluating the experience of patients who receive care from an ANP while a three-factor structure was proposed that can capture specific attributes of this care.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".