Measuring Client Satisfaction With Digital Services: Validity and Reliability of a Short-Form Digital Tool
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Bibliographic record
Abstract
Background Because of the COVID-19 pandemic’s preventive measures, mental health care services were forced to reorganize and develop remote telehealth services. This led to newer modes of receiving treatment, both internet-based and video-based therapies, to meet patients’ need for help, while at the same time keeping the COVID-19 pandemic under control. This shift calls for an evaluation of the patient experience during times of increased use of novel approaches of receiving treatment. Brief evaluation forms are ideal for this purpose. Objective As there are no validated brief measurement tools to evaluate patient-reported experiences in Norwegian mental health settings, we aimed to explore the internal consistency and factor validity of the 4-item self-administrated Client Satisfaction Questionnaire (CSQ-4). Methods We examined the internal consistency and factor structure of a brief digitally administrated patient satisfaction measure in a sample of 145 outpatients in Norwegian mental health settings during the COVID-19 pandemic. Results The internal consistency of a digital Norwegian CSQ-4 was high, with a Cronbach α of .92. A clear unidimensional structure (eigenvalue=3.22), which explained 80.4% of the variance, emerged from our data. A Mann-Whitney U test found a nonsignificant difference in satisfaction between genders (U=2546.5; P=.17). A Spearman rank correlation between satisfaction and age in our data was not statistically significant (r144=.110, P=.19). Conclusions A measurement tool such as the CSQ-4 would be a valuable resource to improve the development and application of digital mental health services. Our results may support the use of the Norwegian CSQ-4 as a valid and reliable measure of satisfaction with mental health care services. In addition, as the CSQ-4 is a short-form and generic tool, it can be implemented in a wide range of routine evaluations of patient-reported satisfaction with telehealth services.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it