Telehealth Technology Competency and Difficulties in the Therapeutic Process
Bibliographic record
Abstract
Background Telehealth therapy services increased during the COVID-19 pandemic and have the potential to shape service provision in the future. The growing body of research on telehealth services provides evidence of the efficacy of such services and the possibility for greater accessibility of counseling services for hard-to-reach clients. However, less is known regarding 2 unique processes of engaging in telehealth services, which are telehealth difficulties and perceived therapist telehealth competency. Objective This study examines the factor structure of the following 2 new measures: the Telehealth Difficulties Scale and the Therapist Telehealth Competency Scale. Methods Exploratory factor analyses were used with 223 participants who used telehealth services. Following this validation, these measures were tested with their association with the therapeutic alliance and therapy productiveness among clients of telehealth services using linear regressions. Results The study found that both measures had a one-factor structure and predicted therapeutic alliance scores. In addition, telehealth competency predicted therapy productiveness. Conclusions The implications for these results are discussed, and future directions are given. Conflict of Interest None declared.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".