Mental health care using video during COVID-19: service user and clinician experiences, including future preferences
Bibliographic record
Abstract
Purpose This paper aims to evaluate service user (SU) and clinician acceptability of video care, including future preferences to inform mental health practice during COVID-19, and beyond. Design/methodology/approach Structured questionnaires were co-developed with SUs and clinicians. The SU online experience questionnaire was built into video consultations (VCs) via the Attend Anywhere platform, completed between July 2020 and March 2021. A Trust-wide clinician experience survey was conducted between July and October 2020. Chi-squared test was performed for any differences in clinician VC rating by mental health difficulties, with the content analysis used for free-text data. Findings Of 1,275 SUs completing the questionnaire following VC, most felt supported (93.4%), and their needs were met (90%). For future appointments, 51.8% of SUs preferred video, followed by face-to-face (33%), with COVID-related and practical reasons given. Of 249 clinicians, 161 (64.7%) had used VCs. Most felt the therapeutic relationship (76.4%) and privacy (78.7%) were maintained. Clinicians felt confident about clinical assessment and management using video. However, they were less confident in assessing psychotic symptoms and initiating psychotropic medications. There were no significant differences in clinician VC rating by mental health difficulties. For future, more SUs preferred using video, with a quarter providing practical reasons. Originality/value The study provides a real-world example of video care implementation. In addition to highlighting clinician needs, support at the wider system/policy level, with a focus on addressing inequalities, can inform mental health care beyond COVID-19.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".