The Telehealth Satisfaction Scale: Reliability, Validity, and Satisfaction with Telehealth in a Rural Memory Clinic Population
Bibliographic record
Abstract
INTRODUCTION: Patient satisfaction is a key aspect of quality of care and can inform continuous quality improvement. Of the few studies that have reported on patient satisfaction with telehealth in programs aimed at individuals with memory problems, none has reported on the psychometric properties of the user satisfaction scales used. MATERIALS AND METHODS: We evaluated the construct validity and internal consistency reliability of the Telehealth Satisfaction Scale (TeSS), a 10-item scale adapted for use in a rural and remote memory clinic (RRMC). The RRMC is a one-stop interprofessional clinic for rural and remote seniors with suspected dementia, located in a tertiary-care hospital. Telehealth videoconferencing is used for preclinic assessment and for follow-up. Patients and caregivers completed the TeSS after each telehealth appointment. With data from 223 patients, exploratory factor analysis was conducted using the principal components analysis extraction method. RESULTS: The eigenvalue for the first factor (5.2) was greater than 1 and much larger than the second eigenvalue (0.92), supporting a one-factor solution that was confirmed by the scree plot. The total variance explained by factor 1 was 52.1%. Factor loadings (range, 0.54-0.84) were above recommended cutoffs. The TeSS items demonstrated high internal consistency reliability (Cronbach's alpha=0.90). Satisfaction scores on the TeSS items ranged from 3.43 to 3.72 on a 4-point Likert scale, indicating high satisfaction with telehealth. CONCLUSIONS: The study findings demonstrate high user satisfaction with telehealth in a rural memory clinic and the sound psychometric properties of the TeSS in this population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".