Teleconsultations for mental health: Recommendations from a Delphi panel
Bibliographic record
Abstract
Introduction: The use of teleconsultations for mental health has drastically increased since 2020 due to the Covid19 pandemic. In the present paper, we aimed to analyze the advantages and disadvantages of teleconsultations for mental health compared to face-to-face consultations, and to provide recommendations in this domain. Methods: The recommendations were gathered using a Delphi methodology. The expert panel (N = 21) included professionals from the health and ICT domains. They answered questions via two rounds of web surveys, and then discussed the results in a plenary meeting. Some of the questions were also shared with non-experts (N = 104). Results: Both the experts and the non-experts with teleconsultation experience reported a general satisfaction concerning teleconsultations. A SWOT analysis revealed several strengths and opportunities of teleconsultations for mental health, but also several weaknesses and threats. The experts provided a set of practical recommendations for the preparation and organization of teleconsultations for mental health. Discussion: Teleconsultations for mental health have the potential to allow access to care for patients in remote and isolated areas. Thus, their use will unlikely be discontinued after the end of the pandemic. In this context, we suggest that the collaboration among clinicians, researchers, and interface designers is crucial to improve usability and user experience for both clinicians and patients. The importance of teaching teleconsultation skills and informing the public on the features of teleconsultations (e.g., data privacy/security) is also highlighted.
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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 | 0.224 | 0.183 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".