Preliminary Validation of a Patient Satisfaction Instrument in the Emergency Department
Bibliographic record
Abstract
Statement of the purpose: The aim of this study was to develop a valid and reliable satisfaction tool that can be utilised in the emergency departments (EDs) of hospitals throughout Sharjah, United Arab Emirates (UAE). Methods: The study followed a cross-sectional study design. The study was conducted during the period from October 2018 to January 2019. The participants were conveniently sampled. The total number of eligible questionnaires for analysis accounted for 207. The data collection tool was developed following a review of literature which yielded 25 statements. Satisfaction levels were measured using a 3-point Likert scale (satisfied=3, do not know=2, dissatisfied=1). The tool was validated through face validity performed by the research team. Content validity performed by a panel of nine randomly selected specialists. Principal components analysis was done to extract the relevant components to the statements on the tool. Results: Scale content validity index= 0.836. Principal components analysis with oblique rotation extracted three components namely; medical staff performance, duration of the encounter, and general impression about the emergency department. Internal consistency for the tool using the split-half Cronbach’s alpha, part 1=0.80, and part 2=0.82. Conclusion: The findings of the present study support the reliability and validity of the Emergency Services Patient Satisfaction Questionnaire for intended use, following an independent sample of patients at EDs in Sharjah, UAE. The scale is recommended for assessing patient satisfaction with service provision to help hospitals in Sharjah determine to what extent they are meeting the needs of their patients.
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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 | 0.015 | 0.022 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".