Healthcare providers' perceptions of virtual‐care with children's mental health in a pandemic: A hospital and community perspective
Bibliographic record
Abstract
AIM: The purpose of the present study was to explore the experiences of a diverse group of mental health clinicians both in hospital and in the community, who were required to rapidly adopt virtual-care practices in the delivery of mental health services to children, adolescents, and their families. METHODS: Mental health clinicians (N = 117) completed the Clinician Virtual-Care Experience Survey assessing the following domains: ease of technology use, client/patient-provider interaction quality, and clinician wellbeing. RESULTS: Although over 70% of clinicians had not used virtual-care to deliver mental health services prior to the Coronavirus Disease 2019 pandemic, more than 80% felt it was easy to operate the virtual platforms. Clinicians were divided in their perceptions of the effectiveness of virtual-care, with only 42% reporting that they felt they were as effective in delivering healthcare services virtually as compared to in-person. Virtual-care was described as being more effective for specific populations, while challenges were described in building rapport and when delivering difficult or unexpected feedback. CONCLUSIONS: Clinicians felt there were some benefits of adopting virtual-care practices, while challenges were also identified. Understanding of the impact of virtual-care on service providers is essential in order to strengthen mental healthcare for children, adolescents, and their families even beyond the pandemic.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".