Exploring Clients’ Experiences of Transitioning Mental Health Nursing Care from an In-Person to a Virtual Format due to the COVID-19 Pandemic
Bibliographic record
Abstract
The onset of the COVID-19 pandemic led mental health professionals to change the way they engaged with clients, often replacing in-person consultations with virtual ones via telephone or videoconferencing. While studies have investigated the delivery of virtual physical health care, only a handful have investigated the delivery of virtual mental health. These specifically focussed on the outcomes of virtual care whether experiential, practical, or empirical. The transition from in-person to virtual care delivery due to the COVID-19 pandemic has been unexplored. Accordingly, the purpose of the study was to: (1) Explore the experiences of clients who had to transition from an in-person to a virtual provision of mental health care due to the COVID-19 pandemic, and; (2) Explore the nurses' experiences of this technological transition. Using an interpretive phenomenology methodology, semi-structured interviews were conducted with nurses and clients who have experienced the in-person to virtual transition of service delivery at a tertiary mental health hospital in Ontario, Canada. In this article, we focus on the results stemming from our interviews with clients. The themes generated from the analysis of client experiences are 1) the psychosocial impact of the COVID-19 pandemic on clients, (2) mixed feelings of clients towards nursing care delivered via technological means and (3) the role of nurses regarding transitioning of in-person care to technology-mediated care. These findings are relevant as mental health care hospitals are considering how they will deliver services once concerns with the transmission of the COVID-19 virus are resolved.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".