Therapist Well‐Being in the Context of Virtual Care: A Qualitative Study
Bibliographic record
Abstract
ABSTRACT Background Virtually delivered therapy (VDT) has become increasingly common, with up to 70% of clinicians reporting using VDT in their practice following the COVID‐19 pandemic. Despite the growing evidence of VDT's effectiveness, limited research has examined the impact of working virtually on therapists' well‐being and professional lives. Given known associations between therapist well‐being and outcomes such as burnout, job satisfaction, and attrition, a better understanding of how VDT affects therapists' professional and personal well‐being is vital to advancing the effectiveness of this mode of delivering care. This study uses a qualitative approach to documenting psychotherapists' experiences of VDT and its impact on their well‐being and professional practice. Methods We conducted semi‐structured interviews with 19 psychotherapists who use VDT in their practices. We analysed transcripts by thematic analysis with an inductive approach. Results Three main themes relevant to therapist well‐being were identified, each with several sub‐themes: (1) personal lifestyle and well‐being (lifestyle changes, self‐care, increased effort impacting well‐being); (2) professional practice (adapting business practices, accessibility to patients, maintaining a professional community, therapists' self‐evaluations of their therapeutic effectiveness); and (3) providing therapy (patient factors, therapeutic engagement, process skills, therapeutic interventions, and the use of technology). Conclusions Overall, therapists reported both benefits and challenges associated with VDT to their personal well‐being and professional lives. Findings highlight ways to improve training, supporting therapists to prioritise physical and emotional self‐care, and using technologies to enhance interventions and streamline practices.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".