A Review of Models and Efficacy of Telepsychiatry for Inpatient Service Delivery: Proposing a Model for Indian Settings
Bibliographic record
Abstract
BACKGROUND: The use of telepsychiatry (TP) for inpatient service delivery is still an emerging field and there is limited literature on its practice and evidence. This review was conducted with the objectives of (a) exploring the models of TP for inpatient service delivery, (b) qualitative synthesis of the efficacy of TP in inpatient settings, and (c) proposing a best-fit model of TP-based inpatient care for Indian settings. METHODS: An electronic database search was conducted on July 22, 2020, in PubMed, Directory of Open Access Journals, and Google Scholar for relevant articles. Seventeen articles were included in the review. RESULTS: The review revealed three models for TP-based inpatient care; direct care model, teleconsultation model, and the collaborative care model. Preliminary evidence suggests that TP is cost-effective and reliable, and that patients and service providers are highly satisfied with this approach. Evidence gaps were seen for some diagnostic categories such as psychosis and for extremes of age groups. Based on the existing models, we propose an Indian model for implementing TP in inpatient settings. CONCLUSION: Promising initial results and the evidence gaps highlight the need for further research in this area.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".