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Record W2490226874 · doi:10.2196/mental.5675

Home-Based Psychiatric Outpatient Care Through Videoconferencing for Depression: A Randomized Controlled Follow-Up Trial

2016· article· en· W2490226874 on OpenAlexvenueno aff
Ines Hungerbuehler, Leandro Valiengo, Alexandre Andrade Loch, Wulf Rössler, Wagner F. Gattaz

Bibliographic record

VenueJMIR Mental Health · 2016
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRandomized controlled trialDepression (economics)VideoconferencingMental healthTelemedicinePsychiatryTreatment and control groupsPatient satisfactionHealth careNursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is a tremendous opportunity for innovative mental health care solutions such as psychiatric care through videoconferencing to increase the number of people who have access to quality care. However, studies are needed to generate empirical evidence on the use of psychiatric outpatient care via videoconferencing, particularly in low- and middle-income countries and clinically unsupervised settings. OBJECTIVE: The objective of this study was to evaluate the effectiveness and feasibility of home-based treatment for mild depression through psychiatric consultations via videoconferencing. METHODS: A randomized controlled trial with a 6- and 12-month follow-up including adults with mild depression treated in an ambulatory setting was conducted. In total, 107 participants were randomly allocated to the videoconferencing intervention group (n=53) or the face-to-face group (F2F; n=54). The groups did not differ with respect to demographic characteristics at baseline. The F2F group completed monthly follow-up consultations in person. The videoconferencing group received monthly follow-up consultations with a psychiatrist through videoconferencing at home. At baseline and after 6 and 12 months, in-person assessments were conducted with all participants. Clinical outcomes (severity of depression, mental health status, medication course, and relapses), satisfaction with treatment, therapeutic relationship, treatment adherence (appointment compliance and dropouts), and medication adherence were assessed. RESULTS: The severity of depression decreased significantly over the 12-month follow-up in both the groups. There was a significant difference between groups regarding treatment outcomes throughout the follow-up period, with better results in the videoconferencing group. There were 4 relapses in the F2F group and only 1 in the videoconferencing group. No significant differences between groups regarding mental health status, satisfaction with treatment, therapeutic relationship, treatment adherence, or medication compliance were found. However, after 6 months, the rate of dropouts was significantly higher in the F2F group (18.5% vs 5.7% in the videoconferencing group, P<.05). CONCLUSIONS: Psychiatric treatment through videoconferencing in clinically unsupervised settings can be considered feasible and as effective as standard care (in-person treatment) for depressed outpatients with respect to clinical outcomes, patient satisfaction, therapeutic relationship, treatment adherence, and medication compliance. These results indicate the potential of telepsychiatry to extend access to psychiatric care to remote and underserved populations. CLINICALTRIAL: Clinicaltrials.gov NCT01901315; https://clinicaltrials.gov/ct2/show/NCT01901315 (Archived by WebCite at http://www.webcitation.org/6jBTrIVwg).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.031
GPT teacher head0.384
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations49
Published2016
Admission routes1
Has abstractyes

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