Telemedicine in perinatal mental health: perspectives
Bibliographic record
Abstract
Background: The prevalence of perinatal mood and anxiety disorders has significantly increased with the COVID-19 pandemic. In parallel, the pandemic has caused a major shift in delivery of care to telemedicine.Purpose: This article aimed to discuss the different advantages and disadvantages of telemedicine for perinatal mental health. Telemedicine has significant benefits for perinatal mental health patients, including increased accessibility to specialized care, direct observation of child-parent interactions in their home environment, and facilitation of collaborative work between obstetrical providers and psychiatrists. Alternatively, telemedicine may impede recovery and contribute to an increase in social isolation. The use of telemedicine by obstetrical care providers may also contribute to a reduction in screening and identification of these disorders.Conclusion: A hybrid model of in-person and telemedicine delivery of care may serve as a durable compromise solution for these women and their families.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".