Effects of Survey Mode on Results of a Patient Satisfaction Survey at the Observation Unit of an Acute Care Hospital in Singapore
Bibliographic record
Abstract
INTRODUCTION: Over the years, surveys have become powerful tools for assessing a wide range of outcomes among patients. Healthcare managers and professionals now consider patient satisfaction as an outcome by itself. This study aims to determine if results of a patient satisfaction survey are affected by the manner by which the survey instrument is administered. MATERIALS AND METHODS: A patient satisfaction survey was conducted from May 2006 to October 2007 in a tertiary level acute care facility. All patients admitted to the observation unit during the study period were invited to participate. Using a contextualized version of the Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospital Survey, data was collected through either a phone interview, face to face interview or self-administered questionnaire. Each of these survey modes was administered during 3 different phases within the study period. RESULTS: Eight hundred thirty-two (832) patients were included in the survey. Based on results of univariate analysis, out of the 18 questions, responses to 11 (61.1%) were related to survey mode. Face-to-face interview resulted in the greatest proportion of socially desirable responses (72.7%), while phone interview yielded the highest proportion of socially undesirable responses (63.3%). After controlling for possible confounders, logistic regression results showed that responses to 55.6% of the questions were affected by survey mode. Variations in response between phone interview and self-administered questionnaire accounted for 87.5% of the observed differences. CONCLUSIONS: Researchers must be aware that the choice of survey method has serious implications on results of patient satisfaction surveys.
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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 | 0.058 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".