Integration of Online Treatment Into the “New Normal” in Mental Health Care in Post–COVID-19 Times: Exploratory Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has necessitated an immediate and large-scale uptake of online treatment for mental health care. However, there is uncertainty about what the "new normal" in mental health care will be like in post-COVID-19 times. To what extent will the experiences gained during the pandemic influence a sustainable adoption and implementation of online mental health care treatment in the future? OBJECTIVE: In this paper, we aim to formulate expectations with regard to the sustainability of online mental health care after COVID-19. METHODS: In an interview study, 11 mental health care professionals were asked about their experiences and expectations for the future. Participants were recruited from a mental health care organization in the Netherlands. The interviews took place between April 7-30, 2020, at the peak of the COVID-19 crisis in the Netherlands. The data were analyzed using a thematic coding method. RESULTS: From the interviews, we learn that the new normal in mental health care will most likely consist of more blended treatments. Due to skill enhancement and (unexpected) positive experiences with online treatment, an increase in adoption is likely to take place. However, not all experiences promise a successful and sustainable upscaling of online treatment in the future. Mental health care professionals are learning that not all clients are able to benefit from this type of treatment. CONCLUSIONS: Sustainable upscaling of online mental health care requires customized solutions, investments in technology, and flexibility of mental health care providers. Online treatment could work for those who are open to it, but many factors influence whether it will work in specific situations. There is work to be done before online treatment is inherently part of mental health care.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".