Modeling the communication-satisfaction relationship in hospital patients
Bibliographic record
Abstract
OBJECTIVES: Although it has long been known that communication with medical professionals presents a strong relationship with patient satisfaction, research on this topic has been hindered by conceptual and methodological issues (e.g. single-item measures, inclusion of idiosyncratic patient characteristics, etc.). Using a more comprehensive and integrated approach, this study had two objectives: to document the multidimensional structure of the Picker Patient Experience-15, and to test a patient communication/satisfaction model that organizes its dimensions in a conceptually logical array of relationships. First, the factorial structure of the Picker Patient Experience-15 was hypothesized to comprise five dimensions: communication with patient, with family, addressing fears/concerns, preparation for discharge, and patient satisfaction. Second, the hypothesized model included positive relationships between all four communications dimensions, on the one hand, and patient satisfaction, on the other. Within communication dimensions, communication with patient was hypothesized to be the incipient factor for other dimensions, and thus to be positively associated with the other three forms of communication. METHODS: This research is based on a single time point design, which relied on administrative and questionnaire data. The study was conducted at a large University Hospital in Switzerland. The sample included 54,686 patients who received inpatient treatment, excluding those who were cared for in the intensive and intermediate care units. Patients filled out, over a 5-year period, the Picker Patient Experience questionnaire (PPE-15) after discharge (overall response rate of 41%). RESULTS: The proposed five-factor structure of the Picker Patient Experience-15 was successfully supported by the results of a confirmatory factor analysis. Moreover, the hypothesized network of associations between communication and satisfaction latent constructs was substantiated using structural equation modeling. With the exception of the association between preparation for discharge and patient satisfaction, the hypothesized model was fully corroborated. CONCLUSION: A more in-depth understanding of patient satisfaction can be achieved when it is studied as a multifaceted phenomenon.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".