Stakeholder perspectives and experiences of the implementation of remote mental health consultations during the COVID-19 pandemic: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Remote mental health consultations were swiftly implemented across mental health services during the COVID-19 pandemic. Research has begun to inform future design and delivery of telemental health services. Exploring the in-depth experiences of those involved is important to understand the complex, multi-level factors that influence the implementation of remote mental health consultations. The aim of this study was to explore stakeholder perspectives and experiences of the implementation of remote mental health consultations during the COVID-19 pandemic in Ireland. METHODS: A qualitative study was conducted whereby semi-structured, individual interviews were undertaken with mental health providers, service users, and managers (n = 19) to acquire rich information. Interviews were conducted between November 2021 and July 2022. The interview guide was informed by the Consolidated Framework for Implementation Research (CFIR). Data were analysed thematically using a deductive and inductive approach. RESULTS: Six themes were identified. The advantages of remote mental health consultations were described, including convenience and increased accessibility to care. Providers and managers described varying levels of success with implementation, citing complexity and incompatibility with existing workflows as barriers to adoption. Providers' access to resources, guidance, and training were notable facilitators. Participants perceived remote mental health consultations to be satisfactory but not equivalent to in-person care in terms of quality. Views about the inferior quality of remote consultations stemmed from beliefs about the inhibited therapeutic relationship and a possible reduction in effectiveness compared to in-person care. Whilst a return to in-person services was mostly preferred, participants acknowledged a potential adjunct role for remote consultations in certain circumstances. CONCLUSIONS: Remote mental health consultations were welcomed as a means to continue care during the COVID-19 pandemic. Their swift and necessary adoption placed pressure on providers and organisations to adapt quickly, navigating challenges and adjusting to a new way of working. This implementation created changes to workflows and dynamics that disrupted the traditional method of mental health care delivery. Further consideration of the importance of the therapeutic relationship and fostering positive provider beliefs and feelings of competence are needed to ensure satisfactory and effective implementation of remote mental health consultations going forward.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".