Online Consultations in Mental Healthcare During the Covid-19 Outbreak: An International Survey Study on Professionals’ Motivations and Perceived Barriers (Preprint)
Bibliographic record
Abstract
Background: While the general uptake of e-mental health interventions remained low over the past years, physical distancing and quarantine measures relating to the COVID-19 pandemic created a need and demand for online consultations in only a matter of weeks.Objective: This study investigates the uptake of online consultations provided by mental health professionals during the first wave of the COVID-19 pandemic, with a specific focus on professionals' motivations and perceived barriers regarding online consultations.Methods: An online survey on the use of online consultations was set up in March 2020.The Unified Theory of Acceptance and Use of Technology (UTAUT) guided the deductive qualitative analysis of the results.Results: In total, 2,082 mental health professionals from Austria, Belgium, Cyprus, France, Germany, Italy, Lebanon, Lithuania, the Netherlands, Norway, Portugal, Spain, and Sweden were included.The results showed a high uptake of online consultations during the COVID-19 pandemic but limited previous training on this topic undergone by mental health professionals.Most professionals had positive experiences with online consultations, but concerns about the performance of online consultations in a mental health context and practical considerations appear to be major barriers that hinder implementation.Conclusions: This study provides an overview of the mental health professionals' actual needs and concerns regarding the use of online consultations in order to highlight areas of possible intervention and allow the implementation of necessary governmental, educational, and instrumental support so that online consultation can become a feasible and long-term option in mental healthcare.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".