Adjusting an Available Online Peer Support Platform in a Program to Supplement the Treatment of Perinatal Depression and Anxiety
Bibliographic record
Abstract
BACKGROUND: Perinatal depression and anxiety are common and debilitating conditions. Novel, cost effective services could improve the uptake and the impact of mental health resources among women who suffer from these conditions. E-mental health products are one example of such services. Many publically available e-mental health products exist, but these products lack validation and are not designed to be integrated into existing health care settings. OBJECTIVE: The objective of the study was to present a program to use 7 Cups of Tea (7Cups), an available technological platform that provides online peer (ie, listener) based emotional support, to supplement treatment for women experiencing perinatal depression or anxiety and to summarize patient's feedback on the resultant program. METHODS: This study consisted of two stages. First, five clinicians specializing in the treatment of perinatal mood disorders received an overview of 7Cups. They provided feedback on the 7Cups platform and ways it could complement the existing treatment efforts to inform further adjustments. In the second stage, nine women with perinatal depression or anxiety used the platform for a single session and provided feedback. RESULTS: In response to clinicians' feedback, guidelines for referring patients to use 7Cups as a supplement for treatment were created, and a training program for listeners was developed. Patients found the platform usable and useful and their attitudes toward the trained listeners were positive. Overall, patients noted a need for support outside the scheduled therapy time and believed that freely available online emotional support could help meet this need. Most patients were interested in receiving support from first time mothers and those who suffered in the past from perinatal mood disorders. CONCLUSIONS: The study results highlight the use of 7Cups as a tool to introduce accessible and available support into existing treatment for women who suffer from perinatal mood disorders. Further research should focus on the benefits accrued from such a service. However, this article highlights how a publicly available eHealth product can be leveraged to create new services in a health care setting.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".